{
 "metadata": {
  "name": "",
  "signature": "sha256:f8be6f824721b6afd3bf6f54b0767fa2292a53548e8d5d95bda4629f5607dc05"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Simulation Results for Interference Alignment"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook shows BER and Sum Capacity results for different IA\n",
      "algorithms when the maximum number of allowed iterations is limited. Note\n",
      "that the algorithm might run less iterations than the allowed maximum if\n",
      "the precoders do not change significantly from one iteration to the next\n",
      "one.  The maximum number of allowed iterations vary from 5 to 60, except\n",
      "for the closed form algorithm, which is not iterative. The solid lines\n",
      "indicate the BER or Sum Capacity in the left axis, while the dashed lines\n",
      "indicate the mean number of iterations that algorithm used.\n",
      "\n",
      "Let's perform some initializations.\n",
      "\n",
      "First we enable the \"inline\" mode for plots."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we import some modules we use and add the PyPhysim to the python path."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.append(\"../\")\n",
      "# xxxxxxxxxx Import Statements xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "from pyphysim.simulations.core import SimulationRunner, SimulationParameters, SimulationResults, Result\n",
      "from pyphysim.comm import modulators, channels\n",
      "from pyphysim.util.conversion import dB2Linear\n",
      "from pyphysim.util import misc\n",
      "from pyphysim.ia import ia\n",
      "\n",
      "import numpy as np\n",
      "from pprint import pprint\n",
      "\n",
      "from IPython.html.widgets import interact, interactive\n",
      "from IPython.display import clear_output, display, HTML\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxx ipywidgets xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "# ipywidgets is available in https://github.com/jakevdp/ipywidgets\n",
      "from ipywidgets import StaticInteract, RangeWidget, RadioWidget\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Function definitions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This section defines several functions used in this notebook. First let's define helper methods to get mean number of IA iterations from a simulation results object."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Helper function to get the number of repetitions for a given set of transmit parameters\n",
      "def get_num_runned_reps(sim_results_object, fixed_params=dict()):\n",
      "    all_runned_reps = np.array(sim_results_object.runned_reps)\n",
      "    indexes = sim_results_object.params.get_pack_indexes(fixed_params)\n",
      "    return all_runned_reps[indexes]\n",
      "\n",
      "# Helper function to get the number of IA runned iterations for a given set of transmit parameters\n",
      "def get_num_mean_ia_iterations(sim_results_object, fixed_params=dict()):\n",
      "    return sim_results_object.get_result_values_list('ia_runned_iterations', fixed_params)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define a function to read the results."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def read_results(scenario_string):\n",
      "    # Examples for scenario_string\n",
      "    # \"2x2_(1)\"\n",
      "    # \"3x3_([1,1,2])\"\n",
      "    Nr = scenario_string[0]\n",
      "    Nt = scenario_string[2]\n",
      "    Ns = scenario_string[5:-1]\n",
      "    \n",
      "    #Nr = 2\n",
      "    #Nt = 2\n",
      "    #Ns = 1\n",
      "    K = 3\n",
      "    M = 4\n",
      "    modulator = \"PSK\"\n",
      "    max_iterations_string = \"[1_(1)_4,5_(5)_120,200]\"\n",
      "    initizalize_with_string = \"['random', 'alt_min']\"\n",
      "\n",
      "    base_name = 'results_{M}-{modulator}_{Nr}x{Nt}_({Ns})_MaxIter_{max_iterations}_{initizalize_with}'.format(M=M, modulator=modulator, Nr=Nr, Nt=Nt, Ns=Ns, max_iterations=max_iterations_string, initizalize_with=initizalize_with_string)\n",
      "    base_name2 = 'results_{M}-{modulator}_{Nr}x{Nt}_({Ns})_MaxIter_{max_iterations}'.format(M=M, modulator=modulator, Nr=Nr, Nt=Nt, Ns=Ns, max_iterations=max_iterations_string)\n",
      "    \n",
      "    alt_min_results = SimulationResults.load_from_file('ia_alt_min_{0}.pickle'.format(base_name2))\n",
      "    max_sinrn_results = SimulationResults.load_from_file('ia_max_sinr_{0}.pickle'.format(base_name))\n",
      "    mmse_results = SimulationResults.load_from_file('ia_mmse_{0}.pickle'.format(base_name))\n",
      "    \n",
      "    return (alt_min_results, max_sinrn_results, mmse_results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define a function that we can call to plot the BER.\n",
      "This function will plot the BER for all SNR values for the four IA algorithms, given the desired \"max_iterations\" parameter value."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#def plot_ber(alt_min_results, max_sinrn_results, mmse_results, max_iterations, ax=None):\n",
      "def plot_ber(max_iterations=5, ax=None, alt_min_results=None, max_sinrn_results=None, mmse_results=None):\n",
      "    SNR_alt_min = np.array(alt_min_results.params['SNR'])\n",
      "    SNR_max_SINR = np.array(max_sinrn_results.params['SNR'])\n",
      "    # SNR_min_leakage = np.array(min_leakage_results.params['SNR'])\n",
      "    SNR_mmse = np.array(mmse_results.params['SNR'])\n",
      "    \n",
      "    # Alt. Min. Algorithm\n",
      "    ber_alt_min = alt_min_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_CF_alt_min = alt_min_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_errors_alt_min = np.abs([i[1] - i[0] for i in ber_CF_alt_min])\n",
      "\n",
      "    # Max SINR Algorithm (random init)\n",
      "    ber_max_sinr = max_sinrn_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    ber_CF_max_sinr = max_sinrn_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    ber_errors_max_sinr = np.abs([i[1] - i[0] for i in ber_CF_max_sinr])\n",
      "\n",
      "    # Max SINR Algorithm (alt_min init)\n",
      "    ber_max_sinr2 = max_sinrn_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    ber_CF_max_sinr2 = max_sinrn_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    ber_errors_max_sinr2 = np.abs([i[1] - i[0] for i in ber_CF_max_sinr2])\n",
      "    \n",
      "    # MMSE Algorithm (random init)\n",
      "    ber_mmse = mmse_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    ber_CF_mmse = mmse_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    ber_errors_mmse = np.abs([i[1] - i[0] for i in ber_CF_mmse])\n",
      "    \n",
      "    # MMSE Algorithm (alt_min init)\n",
      "    ber_mmse2 = mmse_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    ber_CF_mmse2 = mmse_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    ber_errors_mmse2 = np.abs([i[1] - i[0] for i in ber_CF_mmse2])\n",
      "\n",
      "    if ax is None:\n",
      "        fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12,9))\n",
      "    \n",
      "    ax.errorbar(SNR_alt_min, ber_alt_min, ber_errors_alt_min, fmt='-r*', elinewidth=2.0, label='Alt. Min.')\n",
      "    ax.errorbar(SNR_max_SINR, ber_max_sinr, ber_errors_max_sinr, fmt='-g*', elinewidth=2.0, label='Max SINR (random)')\n",
      "    ax.errorbar(SNR_max_SINR, ber_max_sinr2, ber_errors_max_sinr2, fmt='-y*', elinewidth=2.0, label='Max SINR (alt_min)')\n",
      "    ax.errorbar(SNR_mmse, ber_mmse, ber_errors_mmse, fmt='-m*', elinewidth=2.0, label='MMSE (random)')\n",
      "    ax.errorbar(SNR_mmse, ber_mmse2, ber_errors_mmse2, fmt='-b*', elinewidth=2.0, label='MMSE (alt_min)')\n",
      "\n",
      "    ax.set_xlabel('SNR')\n",
      "    ax.set_ylabel('BER')\n",
      "    title = 'BER for Different Algorithms ({max_iterations} Max Iterations)\\nK={K}, Nr={Nr}, Nt={Nt}, Ns={Ns}, {M}-{modulator}'.replace(\"{max_iterations}\", str(max_iterations))\n",
      "    ax.set_title(title.format(**alt_min_results.params.parameters))\n",
      "\n",
      "    ax.set_yscale('log')\n",
      "    leg = ax.legend(fancybox=True, shadow=True, loc='lower left', bbox_to_anchor=(0.01, 0.01), ncol=4)\n",
      "    ax.grid(True, which='both', axis='both')\n",
      "    \n",
      "    # Lets plot the mean number of ia iterations\n",
      "    ax2 = ax.twinx()\n",
      "    mean_alt_min_ia_terations = get_num_mean_ia_iterations(alt_min_results, {'max_iterations': max_iterations})\n",
      "    mean_max_sinrn_ia_terations = get_num_mean_ia_iterations(max_sinrn_results, {'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    mean_max_sinrn_ia_terations2 = get_num_mean_ia_iterations(max_sinrn_results, {'max_iterations': max_iterations, 'initialize_with': 'alt_min'})    \n",
      "    mean_mmse_ia_terations = get_num_mean_ia_iterations(mmse_results, {'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    mean_mmse_ia_terations2 = get_num_mean_ia_iterations(mmse_results, {'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    ax2.plot(SNR_alt_min, mean_alt_min_ia_terations, '--r*')\n",
      "    ax2.plot(SNR_max_SINR, mean_max_sinrn_ia_terations, '--g*')\n",
      "    ax2.plot(SNR_max_SINR, mean_max_sinrn_ia_terations2, '--y*')\n",
      "    ax2.plot(SNR_mmse, mean_mmse_ia_terations, '--m*')\n",
      "    ax2.plot(SNR_mmse, mean_mmse_ia_terations2, '--b*')\n",
      "    \n",
      "    # Horizontal line with the max alowed ia iterations\n",
      "    ax2.hlines(max_iterations, SNR_alt_min[0], SNR_alt_min[-1], linestyles='dashed')\n",
      "    ax2.set_ylim(0, max_iterations*1.1)\n",
      "    ax2.set_ylabel('IA Mean Iterations')\n",
      "\n",
      "    # Set the X axis limits\n",
      "    ax.set_xlim(SNR_alt_min[0], SNR_alt_min[-1])\n",
      "    # Set the Y axis limits\n",
      "    ax.set_ylim(1e-6, 1)\n",
      "    \n",
      "    fig = ax.figure\n",
      "    \n",
      "    return fig"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# scenario = \"2x2_(1)\"\n",
      "# alt_min_results, max_sinr_results, mmse_results = read_results(scenario)\n",
      "# fig = plot_ber(max_iterations=80, ax=None, alt_min_results=alt_min_results,\n",
      "#              max_sinrn_results=max_sinr_results, mmse_results=mmse_results)\n",
      "# a=10\n",
      "# a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define a function that we can call to plot the Sum Capacity.\n",
      "This function will plot the Sum Capacity for all SNR values for the four IA algorithms, given the desired \"max_iterations\" parameter value."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_capacity(max_iterations, ax=None, alt_min_results=None, max_sinrn_results=None, mmse_results=None):\n",
      "    SNR_alt_min = np.array(alt_min_results.params['SNR'])\n",
      "    SNR_max_SINR = np.array(max_sinrn_results.params['SNR'])\n",
      "    # SNR_min_leakage = np.array(min_leakage_results.params['SNR'])\n",
      "    SNR_mmse = np.array(mmse_results.params['SNR'])\n",
      "    \n",
      "    # xxxxx Plot Sum Capacity (all) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "    sum_capacity_alt_min = alt_min_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_CF_alt_min = alt_min_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_errors_alt_min = np.abs([i[1] - i[0] for i in sum_capacity_CF_alt_min])\n",
      "    \n",
      "#     sum_capacity_closed_form = closed_form_results.get_result_values_list(\n",
      "#         'sum_capacity',\n",
      "#         fixed_params={'max_iterations': max_iterations})\n",
      "#     sum_capacity_CF_closed_form = closed_form_results.get_result_values_confidence_intervals(\n",
      "#         'sum_capacity',\n",
      "#         P=95,\n",
      "#         fixed_params={'max_iterations': max_iterations})\n",
      "#     sum_capacity_errors_closed_form = np.abs([i[1] - i[0] for i in sum_capacity_CF_closed_form])\n",
      "    \n",
      "    # Max SINR Algorithm (random)\n",
      "    sum_capacity_max_sinr = max_sinrn_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    sum_capacity_CF_max_sinr = max_sinrn_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    sum_capacity_errors_max_sinr = np.abs([i[1] - i[0] for i in sum_capacity_CF_max_sinr])\n",
      "    \n",
      "    # Max SINR Algorithm (alt_min)\n",
      "    sum_capacity_max_sinr2 = max_sinrn_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    sum_capacity_CF_max_sinr2 = max_sinrn_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    sum_capacity_errors_max_sinr2 = np.abs([i[1] - i[0] for i in sum_capacity_CF_max_sinr2])\n",
      "\n",
      "    # MMSE Algorithm (random)\n",
      "    sum_capacity_mmse = mmse_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    sum_capacity_CF_mmse = mmse_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    sum_capacity_errors_mmse = np.abs([i[1] - i[0] for i in sum_capacity_CF_mmse])\n",
      "    \n",
      "    # MMSE Algorithm (alt_min)\n",
      "    sum_capacity_mmse2 = mmse_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    sum_capacity_CF_mmse2 = mmse_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    sum_capacity_errors_mmse2 = np.abs([i[1] - i[0] for i in sum_capacity_CF_mmse2])\n",
      "\n",
      "    \n",
      "    if ax is None:\n",
      "        fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12,9))\n",
      "    # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "    ax.errorbar(SNR_alt_min, sum_capacity_alt_min, sum_capacity_errors_alt_min, fmt='-r*', elinewidth=2.0, label='Alt. Min.')\n",
      "    #ax.errorbar(SNR_closed_form, sum_capacity_closed_form, sum_capacity_errors_closed_form, fmt='-b*', elinewidth=2.0, label='Closed Form')\n",
      "    ax.errorbar(SNR_max_SINR, sum_capacity_max_sinr, sum_capacity_errors_max_sinr, fmt='-g*', elinewidth=2.0, label='Max SINR (random)')\n",
      "    ax.errorbar(SNR_max_SINR, sum_capacity_max_sinr2, sum_capacity_errors_max_sinr2, fmt='-y*', elinewidth=2.0, label='Max SINR (alt_min)')\n",
      "    ax.errorbar(SNR_mmse, sum_capacity_mmse, sum_capacity_errors_mmse, fmt='-m*', elinewidth=2.0, label='MMSE (random)')\n",
      "    ax.errorbar(SNR_mmse, sum_capacity_mmse2, sum_capacity_errors_mmse2, fmt='-b*', elinewidth=2.0, label='MMSE (alt_min)')\n",
      "    # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "    ax.set_xlabel('SNR')\n",
      "    ax.set_ylabel('Sum Capacity')\n",
      "    title = 'Sum Capacity for Different Algorithms ({max_iterations} Max Iterations)\\nK={K}, Nr={Nr}, Nt={Nt}, Ns={Ns}, {M}-{modulator}'.replace(\"{max_iterations}\", str(max_iterations))\n",
      "    ax.set_title(title.format(**alt_min_results.params.parameters))\n",
      "\n",
      "    #leg = ax.legend(fancybox=True, shadow=True, loc=2)\n",
      "    leg = ax.legend(fancybox=True, shadow=True, loc='lower right', bbox_to_anchor=(0.99, 0.01), ncol=4)\n",
      "    \n",
      "    ax.grid(True, which='both', axis='both')\n",
      "    \n",
      "    # Lets plot the mean number of ia iterations\n",
      "    ax2 = ax.twinx()\n",
      "    mean_alt_min_ia_terations = get_num_mean_ia_iterations(alt_min_results, {'max_iterations': max_iterations})\n",
      "    mean_max_sinrn_ia_terations = get_num_mean_ia_iterations(max_sinrn_results, {'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    mean_max_sinrn_ia_terations2 = get_num_mean_ia_iterations(max_sinrn_results, {'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    mean_mmse_ia_terations = get_num_mean_ia_iterations(mmse_results, {'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    mean_mmse_ia_terations2 = get_num_mean_ia_iterations(mmse_results, {'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    ax2.plot(SNR_alt_min, mean_alt_min_ia_terations, '--r*')\n",
      "    ax2.plot(SNR_max_SINR, mean_max_sinrn_ia_terations, '--g*')\n",
      "    ax2.plot(SNR_max_SINR, mean_max_sinrn_ia_terations2, '--y*')\n",
      "    ax2.plot(SNR_mmse, mean_mmse_ia_terations, '--m*')\n",
      "    ax2.plot(SNR_mmse, mean_mmse_ia_terations2, '--b*')\n",
      "    \n",
      "    # Horizontal line with the max alowed ia iterations\n",
      "    ax2.hlines(max_iterations, SNR_alt_min[0], SNR_alt_min[-1], linestyles='dashed')\n",
      "    ax2.set_ylim(0, max_iterations*1.1)\n",
      "    ax2.set_ylabel('IA Mean Iterations')\n",
      "\n",
      "    # Set the X axis limits\n",
      "    ax.set_xlim(SNR_alt_min[0], SNR_alt_min[-1])\n",
      "    # Set the Y axis limits\n",
      "    #ax.set_ylim(1e-6, 1)\n",
      "    \n",
      "    fig = ax.figure\n",
      "    return fig"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# fig = plot_capacity(max_iterations=200, ax=None, alt_min_results=alt_min_results,\n",
      "#              max_sinrn_results=max_sinr_results, mmse_results=mmse_results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def read_results_and_plot_ber(scenario_string=\"2x2_(1)\", max_iterations=5):\n",
      "    (alt_min_results, max_sinrn_results, mmse_results) = read_results(scenario_string)\n",
      "    \n",
      "    fig = plot_ber(max_iterations=max_iterations,\n",
      "             ax=None,\n",
      "             alt_min_results=alt_min_results, \n",
      "             max_sinrn_results=max_sinrn_results, \n",
      "             mmse_results=mmse_results)\n",
      "    return fig"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def read_results_and_plot_capacity(scenario_string=\"2x2_(1)\", max_iterations=5):\n",
      "    (alt_min_results, max_sinrn_results, mmse_results) = read_results(scenario_string)\n",
      "    \n",
      "    fig = plot_capacity(max_iterations=max_iterations,\n",
      "             ax=None,\n",
      "             alt_min_results=alt_min_results, \n",
      "             max_sinrn_results=max_sinrn_results, \n",
      "             mmse_results=mmse_results)\n",
      "    return fig"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Scenarios to be ploted"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#read_results_and_plot_capacity(scenario_string=\"2x2_(1)\", max_iterations=120)\n",
      "scenarios = [\"2x2_(1)\", \n",
      "             #\"3x3_(1)\",\n",
      "             #\"3x3_(2)\",\n",
      "             #\"3x3_([1,1,2])\",\n",
      "             #\"3x3_([1,2,2])\",\n",
      "             \"4x4_(2)\",\n",
      "             #\"4x4_([2,2,3])\",\n",
      "             #\"4x4_([2,3,3])\"\n",
      "             ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plot the BER"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# #read_results_and_plot_ber(scenario_string=\"2x2_(1)\", max_iterations=120)\n",
      "# StaticInteract(read_results_and_plot_ber,\n",
      "#                scenario_string=RadioWidget(scenarios),\n",
      "#                max_iterations=RangeWidget(5, 120, 10),)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "read_results_and_plot_ber(\"2x2_(1)\", 200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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OLEfmva2scLC3Z3RICNXq1oVy5Qjq1AkqV0Z79ChoNEZfl9RcYlkX3bCx1NNw\n8b3zPk0hH3OPpZ5ST1ONc7eZSj7mHuduM5V8zD0ODg42+ZF5s2zmJ1lZ0bpsWULq1UNTpQrExcGV\nK7r/ZmZC5cq6m4tL3v/e++9KlcDa2tgvx+i0Wq3+jSuentTTcKSWhiX1NCypp+FILQ1L6mlY5jDN\nxiyb+XFOTnRatoyQ3r3z75CSAlev5m3wr1zJ+++4OLh2DZyd79/s37utTBmQOfpCCCGEECWKOTTz\nVsZO4El0WraM6AsXCr7TwQE8PHS3B8nOhoSE/E3/pUuwf3/eDwC5o/0FNf0y2i+EEEIIIYzELEfm\nizzllJS7zf2/R/jv3VbQaP/9PgCYyGi/fB1nWFJPw5FaGpbU07CknoYjtTQsqadhych8ceHgADVr\n6m4Pkp0N16/nb/rvHe3P3fag0f57t8lovxBCCCGEuA8ZmTeWe0f7C5rX/+/R/odN8XFxgdKlH2u0\nXynFJ5MnM3HWLFm3XwghhBDiX8yh7zTLZj4zMxMrqxLypcL9RvsL+gDw79H++30A+P/R/q0//shv\nw4fT8X4nEwshhBBClGDSzBcCjUZDq2bt+WPvNmOnYnoKGu0voOlfdeUK3wO+StEO2GFry3Frawb0\n7MkLixeDjY2xX4nZkrmKhiO1NCypp2FJPQ1HamlYUk/D+nczn5mZyfDhw7l06RLp6elMnTqV+vXr\nExoaioWFBV5eXsyfPx+NRsPixYtZtGgRVlZWTJ06lS5duhRKjmY5vH1gnzvWmvrUqGnD+bDjxk7H\ndDzi3P5BO3bwzP/+x5+7dqFJSSFHKcaUKUPIrl26E3M9PMDLK+/NwwMsLYvohQghhBBCmJ7Vq1dT\noUIFVq5cSVJSEr6+vvj7+zNz5kxatWrFq6++ys8//0zTpk35/PPPOXz4MKmpqbRo0YL27dtjUwgD\npmY5Mg8NgEHYaMZTtdJPuNfMpGe/bgQ0KkNiohZHR+NfMczU47Tr1/lt+HBiypVDxcfz0sqVhPTu\njXbbNoiKIsjODk6d0u0fEUHQrVtQvz7a8uWhRg2CuncHLy+0YWFyxV2JJZZYYokllrhYxv++AmxK\nSgpKKRwdHUlISKBx48ZkZGQQHR0NwC+//MK2bdsICQlh8+bNLFiwAIBevXoxZcoUGjVqhKGZZTNv\nyUt0LhNLD+8ORCW4cCXGg0splYiwtuFSljNOdil4uCfh39iKpq0q4+trQ/36UKqUsbM3HYtnzcK1\nTh069OpBtWW0AAAgAElEQVTFtvXrib5wgRcnTbr/A5KT4cwZOHUq7+3Onfyj+F5eUKFC0b0YIYQQ\nQohCcL8588nJyXTv3p2XXnqJN998k8uXLwOwa9culi5dSseOHTl58iSzZ88GYOjQoQwZMoS2bdsa\nPkllZoACbxPfnKi0635SayZ9pD5r94Wa4varCi11Rrlr3ihw/379pqnz55XKysr7/NOnTy9w/+nT\npxeYj7nvP3ToUMM8f6dOSo0cqVSLFko5OytVsaJSbdqo6Y0bm9TrNZt6yv5q165dJpWPue8v9ZR6\nmur+u3btMql8zH1/qefT7T906FA1ffp0/Q3yt8pRUVGqUaNGatmyZUoppapVq6a/b8OGDWrMmDHq\nl19+UaNGjdJv79mzpzp8+HCBuTwtsxyZf9SUs3OyCTtzhrA9+0k6eo2rxypwNbIWlxNcCbcqRQSO\n3Mi2x61yIg28MmkaVB5fPzu8vXWLv5SE1Rq1Wq3+KyWDUQpiY/OP4p85A+XL5x/Fr1cP7OwMm4OR\nFEo9SyippWFJPQ1L6mk4UkvDknoa1r/7zqtXrxIUFMSXX35JcHAwAN26dWPChAm0bt2akSNH0rZt\nW1q1akX79u05ePAgaWlpNG3alOPHj8uceTDMEkHJd5I5uW8fcftPcPN4JtdOu3IlujZRdyoSbmNN\nRLYz2RoLarkn4d/ImoBm5fD1tcTLS7fku3hCOTkQEZG/yQ8LA1fX/E1+rVpywSwhhBBCGM2/+85x\n48axdu1a6tatq9/2v//9j9dee42MjAw8PT1ZvHgxGo2GJUuWsGjRInJycnjnnXfo2bNn4eRYEpv5\ngiiluBQTwT+793DjyCWST9gSf742sXE1icSJcKtSXMooh6N9OnVqp/Bscyf8Gzni7V2sBpaNIyMD\nLlzI3+THxECdOvmbfDc3sLAwdtZCCCGEKOZknflCUNRFTc1M5cyJI1z66xB3TiRy62QF4iPrEJPg\nToSNNeEaR2LTnan8TDJeXjkENC+Dr5+1fmDZ1FdzNOmv4+7cgbNn8zf5SUng6Zm/yTeBuVEmXU8z\nI7U0LKmnYUk9DUdqaVhST8Myh2beLNeZL0p21nYEBAQSEBCYZ3tcYhyn9/7F9UP/kHkqi6R/3Lh6\nsC7hf1Rkh50VETllSMpwxL3aLfwDrPBv7IS3twXe3lC1qtF7TvNgbw8BAbrbvW7cgNOn7zb3v/4K\nJ0/qpvEUtLJOuXLGyV8IIYQQopDJyLwBZWZn8s/Fk1z8az+3j8eRfcaWhAt1iIutQ4TGnnCbUkRm\nliMTK+rVuoN/E3v8GtpKz2kISkF8fP5R/FOnwMkpf4Pv6QmOjsbOWgghhBAmzJT7zlzSzBeBxDuJ\nnDiyjyv7T5B1+hbpZyuQEFGf6AR3IuwsCbdw4FJqeRzss2jQIBv/Jg74+Fji7Q316+sGqMUTUgqi\novI3+P/8o5uW8+8mv25duSCBEEIIIQDz6DulmTeSHJXDxbgw/tm3hxtHIrD4J5s759yJj6pPZFoF\nXZNPGS7fKYtLhTR8/Kzwa2SLt7cGLy+oXRusDDBJqsTOrcvKgosX8zf5ERHg4ZG/ya9Z86EnQCil\nGDloEF+tXv3/VyoWT6PEvjcLidTTsKSehiO1NCypp2GZQ98pc+aNxEJjQe0qdajduw70vrv9dsZt\nTv5ziKh9B0k7cR2rC3YkX6hD3I56hGtt2WlrQ2RWORLSnKjplo5fo1L4+Fnj7a3rOatXl/n4j8TK\nSjcKX7cu9L7nB5CWBufO3W3uly3T/ffqVd2yRf9u8u8p+G/r1pGwYQPb1q8n5N7nFEIIIYQoJDIy\nbwaUUkQlRXHm8F9cO/QPnEnDMuwZkiI8ib7uTriDBeFWDkSmlSdD2eBZLwvfRrb4+Fjg5QXe3vDM\nM/mfNysrC5fydYm7fg4rQwzzF2fJybqLXv17JP/OHVY5OPB9UhK+aWl8CEwtV47jlpYMGDyYFz79\n1NiZCyGEEOIJmUPfKc28GUvLSuN01HEu7ttPyvHL2FywICesGtcueXEp/RkuOlgQoSlDVEoF7OzB\nx1eDj7+NfhT/9dGdOHDQlcBmEfyxd5uxX455un4ddeoUW1eu5M+lS5kFTLa0pHWbNoSMGYOmbVtw\ncDB2lkIIIYR4AubQd0ozXwxdvX2VE6f3EXfgONmnb2J30Z6Mi3WIvVyfcBsb1t7ZzA31B9AQaI8F\nm1AcoKqLM9Gxvxo7ffOj1bL1yy/57eefibG1pWpqKp3q1yfEwkJ3ddvAQOjSBTp31s29F49E5n0a\nltTTsKSehiO1NCypp2GZQ99plnMrQkNDCQ0NJSgoCK1WC6B/40qsi9sH9YAmPfRxYMtAzl07x7ql\nS+n0YzZHTgVyMfM6WRwnh2isGc+NpBC6dPmdFi0seP31IOzsTOf1mHoc7e9Px/79ORMVReatW0SX\nKgWTJqHduBEOHybo6FH4z3/Q2thAkyYEjRwJLVui3bvXJPKXWGKJJTZWnMtU8jH3OJep5GPusTmQ\nkfkSSLtiCbMnf8uOuNrYcp00nqGHQxJdrUawP7sSf9k+Q3iKC23aWtC9hyVdukDlysbOuhjIyYGj\nR2HTJti8Wbc8Zps2uhH7zp2hShVjZyiEEEKIe5hD3ynNfAnl5uKDe43K7PxzM21bdeZSxFWOhmvZ\ntek7bv8Sge2eBhyP82ZPGXuO3PKgvqcF3Xta062bbr69rJhjAPHx8NtvuuZ+2zZwc7s7HadJk4cu\nhSmEEEKIwmUOfac08yWcVqvVf6V0r8zsTP7at4noHw5g/2clIs81ZbdzKfan1cDG0Z4evWzo2hVa\ntwYbm6LP21Tdr54PlZUF+/bpRuw3bYLYWAgJ0TX3ISEFL0dUzD1xLUWBpJ6GJfU0HKmlYUk9DUcp\nRWmL0iSrZGOn8kBmOWdeFD5rS2uCWvSAFj0AOBN2lCqrfmXwTituHG7OX9/b8dqaqlxOqUTHTlZ0\n66ahU6cS2XMahpUVtGypu82aBdHRusb+hx9g5Ejd+qKdO+uae19f+WpECCGEKGSb1m2iJS2NncZD\nyci8eGzxSVfZ++13ZG65CXv9OUB59tiX5VRiTRo2sqRbN0u6dYM6dYydaTGRlgZ//qkbsd+0CVJT\n786zb9cOnJyMnaEQQghRbCxfuJyl7yylRloNQlNCCVbBxk7pgaSZF08lPSudPRvWk7D+HLZ7PDiV\nVIvdZW04eKsuZcvb0qOnNV27QvPmusFnYQDnz9+djrN/v25+fe5c+zp1ZNReCCGEeICcjBzunLtD\nyokUnIOcKVW1VJ77lVJ8+/a3bF+xndD4UIJUkHESfUTSzJdwhpxbp5TixP49XFj1B6X+KMvlMB/+\nrKjhQKYHSWkV6PKc7gTakBAoXdoghzQ5RT5XMTkZdu7UNfebN4Ot7d3pOK1b62IzJfM+DUvqaVhS\nT8ORWhqW1LNg8T/Ec/3X66ScSCH1Qiq27rY4eDvgNs0NRy/HfPtv/HEjq4av4mLyRQ6qg0bI+NHJ\nWKkwGI1Gg2+zlvg2080vuxwZQZVlPzFg+wFSj/qxZ5eGWX9WZVioB82aW9G9u4auXXWLuIgn5OQE\nPXrobkrBiRO6EfsPPoC+fSEo6O6UHFdXY2crhBBCFIqs21mknErBpoINdjXt8t2vKaWhbLuyVB9f\nHfv69ljaPXjFuEsXLjF42WCe6/NcYaVsMDIyL4pEys3b7Fu5ltsb47Hc58khOzt2Ozhz5Jonru6l\n9PPsGzUCCwtjZ1tMJCTolrzctAm2bgUXl7vTcWTekxBCCDOWfDSZ6xt0I+23T9wmIy4De0973Ca7\nUaF3BYMdxxz6TmnmRZHLycrh8M9biV1zilK7q3M+tSJ/lLfi79v1ycgpR7duVnTtqju3097e2NkW\nE9nZ8Pffd+faR0ZC+/a65r5jR6hY0dgZCiGEEHlkXMsg+3Y2djXyj7Qnbkvk5u6bOPg44ODtgF0t\nOyysDD8aaA59pzTzJZyx59YppYjYf5Iz3+zEapcT12NqoK2cxf5sDyKv1SA42JJu3TQ895xuYNnU\nGbuejyw2FrZs0TX3O3dC3bp359o3bGgSX4+YTS3NhNTTsKSehiO1NCxzrWdGfAaJWxK5ffI2KSdS\nSDmZQnZqNlVfrYrHLA+j5WUOfad8zy6MSqPR4NHMB49mPgDciLxGpa9/otfWMLKu1mLv6QyWn6/C\n+PFe1KtnTbduFnTrBj4+smjLU6lSBUaM0N0yMmDPHt2I/eDBkJQEnTrpmvsOHaBMGWNnK4QQohhQ\nSpGZkIlN+fxXm0yPTSfxt0QcfByoNq4aDj4OlKpWCo38z/6hZGRemKyMG+kcXvkriT9fxnp/TY6X\nzWGXkxNHEvywLuVE925WdOumW7SlVKmHP594ROHhd6fj7NkDAQF359p7esqnKCGEEA+Vk5HDrb9v\n6UfZb5+4TcqpFBy8HWi4p6Gx03tk5tB3SjMvzEJORg7nfvmLyO+PYaV1IdrSht8rWPD3nQZcSahO\nSAfdPPvOnaF8eWNnW4zcuQO//363uddo7k7HCQ6WkxqEEKKEU9kKjWX+QZ7MG5mcCDmBo4+jfl67\no7cj1s9YGyHLJ2cOfac08yWcOc6tU0pxZW8Yp1b8jtppx+3rz6Ctlspe5cE/0V74+VnTvZtu2cu6\ndYt2INkc6/nIlIIzZ3RN/ebNcPgwtGx5t7mvUcOghyvWtTQCqadhST0NR2ppWIVZz/S49Luj7P+/\nikzaxTSaX2uOpe2Dl3o0V+bQd8qceWF2NBoNLoG1cQmsDcDtsCQqLt1Mly1HIPsqB67fYMuqysz+\nOICyzg50//9lLwMDZTXGp6LRQIMGuttbb8GNG7B9+9117cuVuzsdp0ULsMk/J1IIIYT5OtHxBNbP\nWOPg44Bza2eqjq2KQwOHYtvImwsZmRfFSsb1dM6u/p0rP0Vh9Xc1zlW9yQ4nB47caMjNGy506Wyl\nvwqtnNdpQDk5upH63Ok4589D27a65r5TJ/NYikgIIUoYlaNIDU8l5WSKbqT9/1eS8VzjiZOfk7HT\nMwnm0HdKMy+KrezUbC79epTwb4+i+aMi153usK1CDn+neRMeVZ9mTazo9v/TcQw8Q0Rcvaq7UNXm\nzboLV3l43J2O8+yzYCmjOEIIYWhKKWZOnsmUWVMeaRWYk11PcvvEbd28dm8HHHwccPRxxK5O4azZ\nbo7Moe80yWb+999/57vvvmPx4sX57jOHopqTkjJXUWUrEvZEcWbFH2RsL0V6mjV/ut5kDzU4FdGY\nalVK0aO7JV27QuPGT77Mekmp52PJzIR9++7Otb9yRXehqs6ddV+RlCuX7yFKKUYOGsRXq1fLsmQG\nIu9Nw5J6Go7U0nA2/riRFUNXMGDGAAIrBepXknF5xYUKPfJfFTUnMwcLa2naH8Qc+k6T+wlevHiR\nY8eOkZaWZuxURDGisdRQvrUbrZYOoV10f1ppg+nb3pn306NYl7GTYVZLObxjA70HXKGSSxYjRsDP\nP0NKirEzLwasraFVK/joIzh5Eg4d0p3A8O234O6um18/axYcP647yRb4bd06EjZsYNv69cbNXQgh\nzMDyhctp26At60atY9SdUfw8+Wf6jO7Dz+d+psqoKpQJLHheqTTyxYNJjswDDB48mJUrV+bbbg6f\nkIR5SYtNJfL7A1xeF4nFsYpEecSw1cmWI7caEXOpDkGtLenWVXcV2qpVjZ1tMZOWBlqtfq79qqtX\n+T47G9+0ND4EppYrx3FLSwYMHswLn35q7GyFEMIkKaXY+ONG1r+xnqGXh/JN9W/o9d9edOndRb7d\nfErm0HcWyUeyAwcOEBwcDEBOTg4jR46kefPmBAcHc/HiRQDeffddBg4cyI0bN4oiJSH0bKvYUW98\nEG3/CqXllQ4Ev92UV8rBgqhwvqv8De5JX7FoyZ/U80ynYUA2M2bA0aP6QWQ9pRSTJn1s8r/0JsXW\nVjfl5rPPICyMQYcOMbpfP3IADZCTlsaYUaMY9PHHxs5UCCGMLv1KOpe/vEzYhLA82zUaDRqNhtRb\nqSz3XE7KjRT9NlH8FXoz//HHH/PSSy+Rnp4OwIYNG8jIyGDv3r3Mnj2bCRMmAPDBBx/w3Xff4ezs\nXNgpiXtotVpjp2BSrJyscH3Bh6CNQwhK6Eyz+e0YXOsZPom+zjqHdXS3/Zyd234ipGsi1VyzGD1a\nd55nejqsXbuZjz/6gx9/3GLsl2GeNBo09eqh6dqVNKAvkJqdjWb5cjS1asH778OlS8bO0mzJ77ph\nST0NR2r5YOlX0rk8/zJHg45ysP5Bbu69iXNQ/l7p0oVLDF42mKFfDGXIsiFcuiB/L0uKQl91u1at\nWqxfv57BgwcDsGfPHjp27AhAkyZNOHToUIGPK2iKjRBFycLaggodXKnQwRWlFMlHk6j07X5a/nIL\nkndzzvMiW06Xpf/3ydxK/A1oALzJCy/8xOjRXzJ48AA+/fQFY78M86LVEv3VV3Ts2xcbOzsyUlOJ\nTkiA/v3hxAlo2FB3Gz4cevQAOztjZyyEEIVGKcXxNsdxbOhI9fHVKduh7H3XdB89eTSg+3DUpXeX\nokxTGFmhN/O9evUiMjJSHycnJ1O6dGl9bGlpSU5ODhaPsXxIaGgo7u7uADg7O+Pn56c/Ez73E77E\njxbnbjOVfEw5Lt2wHEm37LF4zp4mbk2otuYw6ct+wyHxPPtIIJZUMtGQmXEFzbU0zm75Em3XaiaT\nv1nEQO2pU/PcX0u3gy7u1g327CFo+XIYMwZtixbQuTNBL78MGo3x8zfhOCgoyKTyMfdY6imxweNd\nWtDkv7/16dZocv++7TehfEtIbA6K5ATYyMhIBg4cyL59+5gwYQJNmzalb9++AFSvXp3o6OhHfi5z\nOBFBlCyfLjzM3DfXEXf7Fk7ATTSgaUNd3xas/aYC3t7GzrCYioqCFStg2TJwdIRhw+CFF6BC/uXX\nhBDCFKVfTufaj9eIXxtPpYGVqDpaVlkwNebQd1oU9QEDAwPZvHkzAPv378fHx6eoUxD3MKdPnqaq\nX5Vkatj/TQCnqW9xgMacph1raMHvNG+VRPfetzl/3thZmp+HvjddXeHddyEsTHcC7dGjULs29O6t\nW9M+K6tI8jQX8rtuWFJPwylptcyIzyB6XjRHAo9w0Ocgt4/dxm2KGy4vGeZK2SWtnqIIm/ncM6p7\n9uyJra0tgYGBTJgwgblz5xZVCkIUiupdg1AuN6jbtyIfbptNnb4VsPa4znBNWVaX2YrTjdU0fDaJ\nPoMS5PzNwmBhoZuG8803uhNkQ0Lgww91zf6kSXDunLEzFEIIvbTINFJOpOA21Y3mcc2pt6wez3R+\nBgubIh9fFcWEya4zfz8ajYahQ4cSGhoqcxYlNulYKcUv//mFqEXhVHEoz9qqaWzY60LzVqmsWtqX\nKlVMK99iF585g/b992H7doI8PWH4cLSVK4O9vWnkJ7HEEhfveL0WyplQPhI/URwcHGzy02zMspk3\ns5RFCaeyFVe/vcrFd88TUy6CH8pl89uBXvQYeJX/zaxL+fLGzrCYy8yELVtg6VLQaqFnT91qOC1a\ngKzBLIQwoLRLafo58GkX02gS1gSrMoW+1ogoRObQd1oYOwFhXLmfPIVhFFRPjaWGyoMr0+x8C1qM\naMeE07VZ6PsLSWF/4+6RyJBRB0lKMu0/FMZgsPemtTV06wYbNuim3Hh5wSuvQJ06MHMmXL5smOOY\nOPldNyypp+EUh1rGLYvjcJPDHG50mDv/3KHGjBo0i21mlEa+ONRTPB5p5oUoIhY2FlQdXZWmYc1p\n2aU1U06680XjzcScuoirewLDxu0kOTnH2GkWb5UqwYQJcPo0rFqlm2Pv7Q2dO8OPP+qu/iWEEI/J\n0smSGh/qGvi6i+tSrkM5LKylxRJFwyyn2ciceYmLQ5yZlMna0WtJ2JiAXauKfJ1YipMnc2jd6STf\nL30bJ0dbk8q32MZpaQRdvw5Ll6I9fBjatSNo2jTw9TWN/CSWWGLTiL/Vwg0IGmUi+UhcJLHMmS8E\n5jB3SYjHkR6XzqUPLxH/Qzwn2ySwMNyWmEue9Bn+G59M642TQ1ljp1hyhIfD8uW6W4UKurn1AwdC\nuXLGzkwIYQSpF1OJXxvPtbXXSI9Op/qb1XF9y9XYaYkiZA59p4WxExDGlfvJUxjGk9SzlEsp6syv\nQ8CBAFrb1GZ+tCPvdzrLH1vrUKd2ImOmziEpueStaWmU96aHB8yYARERMHs27Nmj2zZgAGzbBtnZ\nRZ+TgcjvumFJPQ3HFGuZmZDJoYBDHGl+hPRL6dT8pCbNYpuZRSNvivUUhUtOsRbCRNjVtMNzlSe3\nT96m9DsRNEy6zcHnrjJvfRC/rEyhz4gpvD2mH5XK+Rk71eLP0hLat9fdkpLgu+9gyhSIj4ehQ3VX\nm/XwMHaWQohCYlXOilrzalGmeRk0lrLqlTBtMs1GCBN1c+9NwqeEkx6XwYEQW+ZsscSGDPoNX8yo\nEb2pXrGdsVMseY4fh2XL4NtvoUED3TSc3r3B3t7YmQkhHtOd83e4tvYaFQdUxK6mnbHTESbKHPpO\ns2zm5QRYiUtKvGvXLpIPJlPlhyooYFnNf/juDwsqPuPOwBGfUdu1NlXKtyE4ONgk8i0xcbNmsHEj\n2k8+gdOnCRo4UHdRqtRU0GiMn5/EEktccBwF7lHuXFt7jZSYFGgFTf7bBLsadqaRn8QmF8sJsIXA\nHD4hmROtVqt/44qnV1j1VDmKa+uuETE1AiuXUvzetDwffaOhYsVzDBnxMV2f60h9t9FYWFgb/NjG\nYjbvzcuXYeVK3UWprKx0o/WDB+uWwTQhZlNPMyH1NJyiqmXMZzFEzY6iQu8KVOhbgTKBxXMKjbw3\nDcsc+k4LYycghHg4jYWGin0r8uzpZ6kyuBItvo1mW0AiQzr78NEHq+jfrzafL27NkXNTyMq6bex0\nS5aqVWHSJN0FqRYtgjNnoG5d6N4dfv5ZdwVaIYTRubzoQrOYZtT+vDbOrZyLZSMvSiYZmRfCDGWn\nZRO7IJao2VE4tn2GX2u48MlChWudnYx58QN8n21Mw7ofYmNT0diplky3b8PatbrR+gsX4IUXdCP2\nnp7GzkyIYivlTArX1l7j9rHbNFjfAI1GmnXx9Myh75RmXggzlnUri5i5McR8HoNTr0r8VK4qny7M\nwd3nZ8a/NINang0IqPcx9va1jJ1qyXX+vG7d+hUroHp1XVPfvz+UKWPszIQweymnU/TrwGfdzKJC\nnwpU7FuR0s1LSzMvDMIc+k6ZZlPC5Z7gIQyjqOtpVdoK9+nuNP6nMQ5OGtotPsKe0Gt0bdKHsWOO\nMvqtrqz9pT27/g7i5s2/izS3p1Vs3pt16sDMmXDpEkybpluv3s1NN69+1y7IySmSNIpNPU2E1NNw\nnqaWFydeJPtWNnUX16VZVDNqz6utmwtfght5eW+WPGa5znxoaKisZmOg+NixYyaVj7nHxqxnrU9r\nEd4knPAVu+nyd036jXVl1llXRo5YSO1WEYwf1puEeGtqVX+Fbt3eQqPRGL1eJSq2skJrbw9jxhD0\n1VewejXaESMgNZWgUaNg6FC04eGmk6/EEhdRnOt+97du3RqVqfhz75/5738LfIJ8TOr1GDvOZSr5\nmHtsDmSajRDF0J1zd4iYFsHN3TexG1uDpTEVWb46C49WSxg9+D/UqGyNV60PqVRpQLFaAcfsKAVH\njujm1n//PQQE6Kbh9OgBtrbGzk6IQqeUYubkmUyZNSXPaLpSipQTd6fQVB5aGbcpbkbMVJRU5tB3\nSjMvRDGWfCSZ8CnhpJ5PxWqsB1+dqsDa9Zl4tPuKoX3/g1elbGrXmELVKi9jZeVo7HRLtrQ02LBB\n19gfOQIDBuiuNNuwIZTgKQOieNv440ZWDV/F4GWD6dK7C+mx6Vyef5lra6+Rk5FDxb4VqdC3Ak7P\nOpXoqTPCeMyh75RmvoTTarX6r5TE0zPVet744wbhk8PJTs5Gja7J/3aXZctvmdTouICeXWfQtFIG\nrtVG4+463mRWwDHVWhaJqCjdCbPLloGTk260ftAgKF/+iZ+yRNezEEg9n87yhctZ+dlKPDI9eP7C\n83xb+1vCrcMZ8PwA2txso2vgG0kD/yTkvWlY5tB3Whg7ASFE4XNu7Yz/X/7UmFkDzZcXmRB+lI2f\npOKeOo7PxsXxzsr/sPTvr/hzrzunz75IaupFY6dcsrm6wrvvQlgYzJsHhw5BrVrQpw9s3gxZWcbO\nUIinMmjAIF6f/jrZadlo0JCdls0b77/Bi1NepObHNSn9rKxGI8SjkpF5IUoYlaOI/y6eiGkR2NWy\n4+bgWsz+1oHjJzOo0W0Rvk0n06N6NuXLdaCm+1RKl25k7JQFwM2b8MMPumk40dEwdKhuGk7t2sbO\nTIhHopTi1t5bxC6O5fqG61ybcY01U9dgW92W1OhUhiwbQpfeXYydphB5mEPfaZbN/NChQ2U1G4kl\nfso4JyOHdW+v4+rKqwQHBxPX04M3Pz7AlfhMGgy8QBWviXjdUJS2r0n/vnMoW7YDf/zxh8nkX6Lj\nihVh2TK0X38NVasSNH489O2L9tAh08hPYonviQO9A7nyzRUu/u8i5IDHax5UHlKZKe9MoWL1ikx8\ndyKb129G+5uWLs93MXq+Ekt8bxwcHCzNvKGZwyckc6LVavVvXPH0zLGe2Xeyufz5ZaLnRFOu6zNE\ntPPg/f/ZcPN2BrV6LcPKbTzDPGypYF+RmjWmUqFCvyJZAccca1nkMjN1026WLoU//4SePXXz6wMD\n85w0q5Ri5KBBfLV6tUxdMBB5fz66y19e5ta+W7i85EKZlvnXgJdaGpbU07DMoe80y3XmhRCGY2lv\nievbrri84kL0J9GUHvs3qwZX5mQjN2bMeQUsh7O6zzcklH2dF5PfpJr9W9RwexsXlxFYWjoYO/2S\nzdoaunfX3a5cgVWr4OWXdXPqhw/XzbM/dYrf3n+fBGBbVhYhnp4QFKS7CVEEqo6qStVRVY2dhhDF\nlgt/7u4AACAASURBVIzMCyHySL+STtR/orj67VVcRlflbw9X3p9tSbnymdTvv5ozlq/zYk1Hatmn\n4FptLFWrjjGZFXAEurXrDxyApUtZtWoV31ta4nv7Nh8CU2vX5ri1NQNee40XXnnF2JmKYkJlKxK3\nJXJt3TXqLqyLxlK+/RHFhzn0ndLMCyEKlBqRSuR7kSRuTaTKBFe0ZasyY6YFNWtl0WDA9+xOHc+Q\nGnb4OSVRpfILVK8+ATu7msZOW9xD3b7N1kmT+HP+fGYBk52daf3ZZ4S88IJMtxFPLS06jStLrxC3\nNA6bija4vORC5dDKWNhYGDs1IQxCKYWFRTmUSjJ2Kg8kv3ElXO4JHsIwilM97WrYUX9Fffx+9yNl\n303qzTjArrdi6dXDgnXTX6Dq9jiu3/qY106U4fuzm9h/sCGnT/cnOfmwQY5fnGppLBpHRzRBQaQB\nfYHU27fRjB6NZsYMSEw0dnpmraS/P8OnhHPI7xAZ8Rl4/exFwMEAqrxc5Yka+ZJeS0OTehrOunW/\nAb2MncZDyZx5IcQDOTRwwOsnL24duEX45HACYqL5a3YN1sVX4OM3+tM6qC+lBv7M66fepl2F/XRJ\nCKFcaV9cXd+mbNn2MgJsZNEXLtARsAEyvv+e6L17dUtb1q6tW9py/HioUsXYaQoz4zLCBbepblja\nWxo7FSEM7vPPVzFnzveUKuULLDF2Og8l02yEEI9MKUXSjiQipkSgshUV3q3BqjPlmDdPQ9euOTQa\nuJWvw6fg55jAAFcNzrblcHV96/9XwJGxgyKn1epu/xYUBDVrwqefwjffQL9+8NZb4OFRxAkKU5aT\nnkPK6RScGjoZOxUhCt3Fi7rFwTZvht27FW5uW7l5808uX56Fqbed0swLIR6bUorr668TMTUC64rW\nlJviwZI9ZfjyS+jfX9Hs+d/56vy7VLSI5OWaTjhbpVO9+nhZAccUXbsGn30GCxZASAhMmgTe3sbO\nShhRytkU4pbEcXXlVcq0LIPXOi9jpyREoerXD3bvhk6doHNnaNcOduzYyvDhv5GcfAalfjN2ig9k\nls28XDTKcPG8efPw8/MzmXzMPS5p9fx95+8kbUuiyvdVcPRx5FTbGH7aa8eOHUEMH65wrvMFGyJX\n4OgWzRv1XYg6Hkn58t3p3XsONjYVHvj89877NJXXa87xQ+t56xbaiRNh7VqCWrSAKVPQpqWZTP6m\nFhfL9+dULfwCNtdsqDysMlENoqBq4R8/d5vRX38xiXO3mUo+phRnZ2to27Z1vvvj4+HUKS0WFnf3\nf+mlyf/H3n3H13S/ARz/3OwhQwghIoOEiJWEqFGiNqVqtHZi94faFKU2NWpUqVWxSxtbrVKJUiND\nzCAkIZEI2SE79/7+OKVVK5Gb3HuT7/v18mq/cu45z/k6SZ577nOeLzY25Zk5c6La30TWyGRew0JW\na35+fi8uXKHwSut8yrPkxKyN4f7C+5RtVRa9EfYs22HI7t0wciS06BXEqqtziXxylql1q1FZ6w4V\nK/b5uwOOw2v3WVrnsqjkez4zMsDHB5YsAXt7mDpVuk0lnn14SUm8PiPnRWLsYky5j8uhpatVbMct\niXOpSmI+/5GdDefO/VM+07UrzJ9fsH1oQt4pknlBEJQmNy2X6BXRRK+MpkLPCsi9bFm0Tp/Dh6Xn\nLD/6/DorLy/g0oNjTK9fi2q6oViUbUPVqpMwMXFXdfjCv+XkwK5dsHAhGBvDtGnS4lRaxZfkCYIg\nvI/QUJg+HU6dAicnqXSmY0do0KDgP8I0Ie8UP5UFQVAaHRMd7GbY0eh2I7TLaBPfKYDpFe5x+nAO\nV67AJ01q4xG1kwOfB3AuzZnPz8vxi43jytXOhIS0JjHxBAqFAoVCwfz5U9T+B2iJpqsL/fvD9evw\n9ddSUl+7tvTAbE6OqqMTCkihUJB6MZVbQ25xe9htVYcjCEXKzEy6C3/nDly6BLNmgYdHyb0XUUJP\nS8ivf9fYCYUn5lOiW06Xakuq0fBqQ3KTc0nudJFFde5zZH8up09D+0bVaBC7gbMDr3Inpy7dzmVw\n/JGcG6GD+euvyixerMWxY4vYvPlzIiJmkZTkp+pT0njvfW1qaUm/FS9elB6U3bJFamu5erVUklNK\nacr3ek5SDtGrogmsF8jNvjcxrG6I3Rw7VYf1Ek2ZS01RGubz8WPpvsLgwSCXv/r1ypWlexEVSsni\n5CKZFwShyOhb61NjXQ3czrvx7PozMj+9xOrW0fyyU86ePdCmkQ3uT1Zy5YvbJOs0ovMPTxg8JoHg\nEOjWDQ4dPcgnfVazdNMhVZ+KIJNJtfOnTknlNydOSK0sFy2C1FRVRye8hjxbTkDtAFL/SqX6iuo0\nutMI2ym26Fvpqzo0QSiwgACYORMaNpRKZw4cgCZNIDdX1ZGpnqiZFwSh2KRdTiPi6wjSQ9Oxm23H\nrSoVmf6NjMREmDMH7j9ezuLvZ1LbIY2K5ubk5CZj76BDnXrD6NfrB7EAlbq5fh2+/RaOHYMvvoAx\nY8DSUtVRCf+Sl5GHtqFY2EnQfEOHQtmyUu17kyagp1c8x9WEvFMk84IgFLvkP5MJnxpObnIudnPt\nCTIoz4wZMnJy0qhTdxa3bq8lJLgXrm67aNJGn886lcHI0B4Hh0WYmX2g6vCF/woPl7rf7N4NAwbA\nhAlgY6PqqEoFhVxayE3bRBuzxmaqDkcQ3ptcDiEh0vP2NWqoOpp/aELeKcpsSrnSUFtXnMR85o/5\nh+a4/ulKtcXVuD87kgqzgjmxKAljy5/Ysf0mQQEjycvrR2BQf35Y4kTrryoTluXAjRs9uX79U549\nC1X1KWicIr02HRykRaeuX5cenK1fXypmvXOn6I6pYqr+Xs96mEXkvEguVrtI+JRwchM1t9ZA1XNZ\n0mjSfCYnw6+/wqBBYG0NvXvDtWuqjkrziGReEASVkMlklOtYjgbBDagytgp3ht9mDZ54dawOZAIy\n9HQM+axPPc7sWsXKm3cYd70cCfJKhIS04NatwWRmRqn6NIR/q1xZukMfFga2ttCsmbS04uXLqo6s\nxMiKzeLaJ9cIqB1AVnQWLr4uNAhuQLlO5VQdmiAUyJEj0gd4Pj7g5gZnz8Lt29Cjh6oj0zwaWWYj\nVoAVYzEueWN5jpwRH4zgcPBJ4miNFRBNHHoye2YPrcdXawcwc/NM1gWto41nYybUsubsqW1YWHTg\ns89+RFfXQq3OR4zB7+hROHQIz4MHoW5d/Nq3h7p11Sc+TRxnQ42HNbDsacnZwLOqj0eMxfgd46ZN\nPdHVffXrx4+fQaGA9u2bq1W8/x23bNlS7ctsNDKZ17CQBUHIp9OnFYwZtYDkmzm0xAs/7W1kWZiR\nKx+Nr68MT09Iz0ln6V9LWXlxJWPc+9Kt8lNSEg9RpcoEqlQZjba2kapPQ/ivrCypj9yiRVCpkrQA\nVfv2YlXZt5Bny0EBWvpaqg5FEApEoZDusD9fdfXWLbh/H7Q19DlsTcg7xU+JUu75O09BOcR8Fk7L\nljIWzK5HI24QiTcNFVeZ7WTA9k1yeveW1i0y0DbimxbfcOWLK9xJSaTd0eNEGkwgLS2QixediIlZ\nj1yuufXDRUWl16a+vtSK4tYtGDECvvpK+lz9l18gL091cRVCUc1n+p107k2+x3mb8yQcTSiSY6gb\n8XNTuVQ5n5MmSY/QtG0rJfRffimtxqqpibym0FF1AIIgCM8l+SURsjaEjj07om+oT+bTTO6dv0fd\n0Zfw31kP76+NOHdOuslbxaIK27tt50L0BcYcG4NcIWdZi5k8fvwzUVHf4eCwgPLlu4l2lupER0d6\nwq1XL/jtN1iwQFpz/auvpBVe9PRUHaFK5GXmEb8nnpgNMaSHpmPlZYXrn64YOYlPmQTN4uEBXl7g\n4iI+eCtOosxGEAS1F+sTS/jkcGyXVWfZ5Yrs2yd1QGjQQPq6XCFn57WdTDk5hQ9tmzHToxNP45Yh\nk+nh4PAtZcu2VO0JCK+nUMCZM1JSf/MmTJwIQ4ZIvelKkaQ/kniw6AGVhlaifJfyaOmJD80F9ZOZ\nKX27Hj0K7dpJlXKlgSbknSKZFwRBI6SFpHGjxw0s2ltwpVl1RnypxZw50lpFz+8APct+xqJzi1gd\nsJpRDUcy2MmemKh5GBo64uCwEBMTV9WehPBmQUFSHdWff0qfzY8cKa0QIwiCyjx6BPv3S7Xvfn5Q\np460aFOvXlCtmqqjKx6akHeKt/+lnKhVVC4xn8rz37k0qW9Cg6AGZD/MxmH5ZU77ZrF2LfTrB0+f\nStsY6xkzp+UcgocFczvhDk1/+YZ7ht9QrtzHXLvWkZs3+5CRca/4T0YNqP216e4Ovr7g7w/37kH1\n6jBlCsTFqTqy1yrofKYFpXH7i9tkxWYVTUAaTO2vTQ2jzPkMDoZz56TquIgI6f+//rr0JPKaQiTz\ngiBoDB0zHVz2umDZ05KUzwM5PDsBfX2pTjP0X+tI2ZrbsqvHLn7u/jPLL66i5/EdaFXZiZGRM0FB\njbhzZxTZ2eqZJJZ6NWtKjaeDg+HZM3B2lu7SR0aqOrICy03J5eGahwS6BXKjxw0MbAxEdxpB7Tx8\nKJXOvE7HjrBtm5TMlxNLGagtUWYjCIJGSv4zmZu9b1JpcCX+sLFjylQZK1dCnz4vbydXyNl6ZStf\n//E1H9l/xLzmk8hO3ERc3DasrUdhYzMBHR1T1ZyE8G5xcbByJaxbB506SXfra9VSdVTv9GjbI+6O\nvkvZNmWpNLQSZVuVRaYlnggUVC83Fy5c+Kd1ZFQUdOkCmzaJh1ZfRxPyTnGLQBAEjWT+oTnuge6k\nnEmhwS9XObI7h5kzpZu4Wf+qZNCSaeFd35tbI29hY2qD208t2RVTjtr1zpGZGcHFi05ER69ELhfl\nD2qpYkXpAdl796S79h99BN26QUCAykJSKBTMnzL/rb/gy7Yqi8dtD1x+ccGijYVI5IVioVAomDJl\n8RuvTYUCatSA0aOlxH31aun9so+PSOQ1mUjmSzlRq6hcYj6VJz9zqW+lT93f62LSwIRc70BOrUnh\n0SNo1uzVqgwTfRMWtFpA4NBArsRdod5P7bia14m6dY+TmPg7ly7V5NGjbSgUmtn3/F00/to0N5cW\nmwoPh5YtoXt3aNMGTp+WMpRi9Nue3whZFcKRPUdIDUx97Tb6lfXRq1A6W20WlMZfm2pkz57jrFoV\nha/vCbKzX/26TCZVsAUHw/z50LSp1DFW0GwimRcEQaNp6WjhsMABx9WORPW7zorm0fTqpaBRI6mV\n+X/Zl7XH9zNftnbdysKzC+noO4qc8rOpWXMLMTFrCAx0IyHhiNp/rFpqGRlJ3W7u3oW+feF//4Mm\nTeDgQZDLi/TQm9dtppVLKw58dYAR6SPY1XsXHZp1wOd7nyI9riC8y7p126lV62PGjPmT9PTv6dXr\nDNWqfcy6ddtf2dbMTAUBCkVK1MwLglBiZIRncKPnDQyrGxI/pAb9BunQvz/MmfP6u0958jx8QnyY\ncXoG7au3Z37L+ehmXSIiYhq6upY4OCzCzOyD4j8RIf/y8mDfPqmtZXY2TJ0Kn31WJLcbFQoFu2fs\n5tDCQwyVD2Vzxc30WNWDTj06icXJBJWJj4cNGxQsW3aMlJQz5OQspHLlqaxc2YLu3duJa7OQNCHv\nFHfmBUEoMQwdDHE954qOuQ4GXwZxZsczAgKkaoxHj17dXltLmyFuQ7g96jaWRpbUXVuXjbdCqeN6\nCSsrL27e7Mn165/y7Fnoqy8W1IO2NvToAYGBsHSp9KBsjRrSfzMzlXqopFNJPFz9kFyDXDbX2kxG\negYyLZlIlgSVunkTwsJkTJggw8Agk1q1xpOWloFMJq7N0kIk86WcqFVULjGfyvO+c6ltoE2NdTWw\nnWZLTPcQNvd/RPPmUhvzM2de/xpTfVMWt1nMhSEXuPjwIi5r6vJXkhkNG97G1LQpISHNuXVrMJmZ\nUe9/QipW4q9NmUxaltLfH7ZuhUOHpGbYS5dCWppSDmHewhzdL3QZsHUAXj94McBnAPfD7itl36VZ\nib82i1jz5lInGoUiCh+f9vzwQ2d8fDoQFqa5P6+EgtHIZN7b2/vFN7+fn99LPwjEuGDjkJAQtYpH\n08diPtVnbDXAitRFqRyYtpe+j++wYa2crl39GD7c78Xzkv99ffTVaMZajWVD5w3M9p9Ng+lNOBFo\njodHGHp6FdiwwYUdOz4nJydR5ecnxm8ZN20Khw/jN3s2fkeOgIMDzJqF34EDhdr/mXNnqNuuLp26\nS2U1xuWMqfVBrffenxiLcX7HISHQpUsMvr5/vXH7xo0dKVdOH5lMRvfu7fjgg+pqE39JGKszUTMv\nCEKJlpuay61Bt8i6n4XJChcGTDCgYkXYvBnKln3L6+S5bAzeyEy/mXRx6sK8j+ZhrptHZORs4uP3\nUqXKBKpUGY22tlGxnYvwnsLCYPFi2LsXvL1h/HiwtlZ1VILwVhkZ8Msv8OOPEBMDw4bBiBFgYaHq\nyEoXTcg7NfLOvCAIQn7pmOrg8qsLFfpW4Em3IPZNScDOTiq7CQ5+y+u0dPiiwRfcHnUbE30TXNa4\n8H3gDuyqfY+r61mePg3i4kUnYmLWI5fnFtv5CO/B0RE2bIArV6Q2lnXqSJnR3buv3VwhVxC1PIqc\nhJxiDlQQJHv3QtWqsGvXPx1Zp08XibzweiKZL+U05SMkTSHmU3mUOZcymQybsTbU3lubyC/vMMYk\nnIULFLRrB+vXv71NubmBOcvaLeOvwX9x5sEZav9Ym98f3KJWrV+oXXsvjx/vIiDAhSdP9qj13Rtx\nbQJVqsCyZXDnDlSqBI0bS0sGX736YpPclFyuf3qdJ75PkOe8udWlmE/lEXP5qoYN4eJFOHpUWp21\nIM2ZxHyWPiKZFwSh1DBraoZ7kDupF1KpseEKfxzIZtUq8PKCZ8/e/lqnck4c6n2I1R1XM+2PabTZ\n1ob7GYbUq3cKR8fvuX9/HsHBH5CUdLp4TkZ4f+XLw+zZ0qqyrq7Qvj107syzn88R5BGEfhV96p+u\nj76VvqojFUq4J09e//c2NtKjHoKQH6JmXhCEUkeRpyByViSPNj/CbnMtpmw1IygIfH2hZs13vz5X\nnsvawLXM8Z9Dd+fuzGk5h/JG5Xj8eDcREdMxNHTEwWEhJiauRX8yQuFlZvJkjC93Nprh4PgHlb7v\nIPUzFW39hCIgl8PJk7B2rdR8KTQUKlRQdVTCm2hC3inuzAuCUOrItGXYz7XHaZ0T4X2uM7duFKNH\nK/jwQ9i9+92v19HSYZTHKG6NuoWuti611tRixYWVlC3fHQ+PUMqV+5hr1zpy82YfMjLuFf0JCYVj\nYEBGtebUOduMStPdpQdkGzaUCpeLeFVZofRISJA6pdaoAZMnS51U798XibxQeCKZL+VEbZ1yiflU\nnuKYy3Idy+F+yZ0nux7T5MQNjuzNZdo0+PJLyMp69+stDC34vsP3nPE+w4nwE9T5sQ5H7/6OtfVI\nPDzCMDKqSVCQB3fujCI7O67Iz+dtxLX5dlUnV8W0cVno10+qoZ8xAxYtAhcX2LIFcnLAzw9mzUIh\nkzFcJkMxcybMmiX9vfDeSsu1uX69dGlt3QqXL8Pw4VCmjPKPU1rmU/iHSOYFQSjVDGwNcD3ril4F\nPeSDg/Df+pSoKGkhlvv5XA/I2dKZo32PsrzdciacmECHHR24k/QAO7tv8PC4hUymw6VLtYiImElu\nbmrRnpBQeFpa8MkncOEC/PADbNsmdcS5fh2++orjQAJwom5dKZn39FRtvIJGmDpVSuQbNxYVXIJy\niZp5QRCEv8XtiOPu2Ls4LKnGjngrliyR+tF36JD/feTk5bA6YDXz/5xPL5dezG45GwtDCzIyIomM\n/IbExBPY2k6lcuUv0NISD1iqQk5yDrrmugV70aVLbB82jF3Xr1MvL495wHRHR67o6tJr9Gj6DR9e\nJLEKmuXaNdizB2bOFAl7SaEJeae4My8IgvC3in0rUt+vPg++vU/n27fZvSOPoUOliou8vPztQ1db\nl7EfjCV0ZChyhZyaP9Rk1cVV6OhZ4+y8lXr1TpCY+DuXLtXk0aNtKBT53LFQaPJcOfcm3+N6l+sF\n/+Xs4UHfy5cZuXgxckAGyOPiGDVuHH2HDSuKcAUNkZUFO3ZAs2ZSYySA7GzVxiSULiKZL+VEbZ1y\niflUHlXNpbGLMe4B7uSm5VJm8mXO7s3g3DnpYbXHj/O/n/JG5VndaTV/eP3BwTsHqbe2HsfvHqdM\nmbrUrXuYmjW3EBOzhsBAVxISjhT5nZ/Sfm1mx2dztf1VnoY8pfa+2sje47apTCZDVrUqmUBPICMj\nA9mYMchGjYLISGWHXGpo8rX5ww9SG8nNm6XnpiMjpcorfRV+6KbJ8ym8H5HMC4Ig/IeOiQ61fq6F\n1UArHnYO5ufR8XzwAbi5wdmzBdtX7Qq1OdHvBN+2/pZRR0fRaWcnbsffxty8Oa6uf2FnN4d79yYS\nEuJJSsqFojmhUi7tchrBDYMxaWBC3aN10S1XwBKbf4kKC6M9MALo8PPPRI0dC6am0pLC/ftLdfVC\nqVG/Ppw7B7//Dt26ge77X1qC8N5EzbwgCMJbpFxI4eZnN6nQpwKhTe0ZPESLyZOlu3AFvbmblZvF\nqkur+Pbst/Sv259vWnxDWcOyyOW5xMVtJTJyJiYmDbC3X4CxsXPRnFApkxWbRWD9QBx/cKRCTyX1\nAHz+D//v30UpKfDjj7BypdTWcupU6UlHoUTIylLt3XZBdV6Xd168eJEpU6Zw+vRpLl++TOfOnXF0\ndARgxIgR9OzZkw0bNrB+/Xp0dHSYPn06nTp1KroYRTIvCILwdtlPsgntG4o8W47xklr0G6mPtTX4\n+IC5ecH39/jZY2b8MYP9t/czq8UshroPRUdLh7y8DB4+/IGoqMWUK9cFO7tZGBjYKP+ESpns+Gz0\nyusVfkd+fq9vQ+np+U9Hm4wM6cJYsgRsbaWkvm1b8TSkBlIo4PRp6T3azZvShy7in7H0+W/euXjx\nYrZv306ZMmX466+/2LhxI6mpqYwfP/7FNo8ePaJt27YEBQWRkZFBs2bNCAwMRE9PCT+HXkOU2ZRy\norZOucR8Ko86zaWepR51j9bF3NOc+K5BHFqQjLU1NGgAISEF318F4wqs67yOE/1O8MvNX3Bd58qp\n8FNoaxtSteokPDzC0NOrQGBgfe7dm0ROTmKhz0Gd5rO4KSWRBylhnzULZs3C71///1JrSkNDGDEC\nwsJg6FCYOFEqwfnll/w/RV3KqNu1mZQEK1aAszOMHi398/71l+Yk8uo2nyVN9erV2bt374sEPygo\niN9++40WLVowZMgQnj59yqVLl2jatCm6urqYmppSvXp1rl69WmQxiWReEAQhH2TaMuxn2VNzU03u\n9rvBZNsHzJ2roE0b+Omnlysu8queVT3+GPAHsz1nM/TQUD7Z9Ql3E++iq2uOg8NCGja8Rm5uKpcu\n1eD+/W/Jy0tX/okJRUNHB/r2hStXYPbsf7LDjRvztyKZoDIDBsClS7Bhg9RqcuRIMDNTdVSCuujW\nrRs6Ojovxo0aNWLp0qX4+/vj4ODA7NmzSUtLw+xfF42JiQkpKSlFFpMosxEEQSigzAeZ3PjsBvqV\n9JFPqUHvQbo0bAhr1oCR0XvuMzeTFRdWsPSvpQysP5DpzadjZiD9MkhPv014+Nekpp7Hzm4mVlaD\n0NLSecceS5/YTbEYOhli3uw9ap+KmkIBZ87AwoVSvcb48TBsWNEsASoUilwurRsmlE5+fn4vfbox\ne/bsV/LOyMhIevfuzfnz50lJSXmRuIeGhvLll18yZswYjh07xurVqwHpDcD06dNxc3MrkpjV6nI9\ndeoUw4YNo1+/fkX6cYQgCEJhGFQ1wPWMK/pV9EnvG8SpjWnk5sIHH8CdO++5Tx0DpjSbwvUR10nM\nSKTm6ppsCNpAnjwPI6Ma1K7tS+3a+3j8eBcBAS48fuwrbmz8TZ4t587/7vBg8QN0y6tpOxGZDFq0\ngGPH4NAhuHgR7O2l1YUSElQdXalz8yb8+uvrvyYS+dLN09OTWbNmvfjzLu3btycgIACAkydP0qBB\nAzw8PPjzzz/JysoiJSWF0NBQateuXWQxq9Ulm5GRwfr165k4cSInTpxQdTilgqitUy4xn8qj7nOp\npaeF4ypH7OfZc6/LVRZ7xjJyJDRt+uYkIT+syljx0yc/cbj3YbZe3UqDDQ3wj/QHwNTUg3r1TuHo\n+D0PHswnOLgRSUmn87VfdZ/P95UVk0VIyxCyH2Xjfskd45rGxXLcQs2nqyvs3i0VYsfEgKMjjBsH\n0dFKi0+TFNe1mZ0tTbunJ7RqBffuFcthi11J/V5XN8/Xqli7di3jxo2jZcuWnD9/nunTp1OxYkVG\njx7Nhx9+SKtWrViwYEGRPfwKgELNPH36VDFw4EDFkydPXvt1NQxZo50+fVrVIZQoYj6VR5Pm8unN\np4qLzhcVoQNDFRfP5irs7BSKMWMUiqyswu1XLpcrdl/frbBdbqvotrub4l7ivX99LU/x6NFOxfnz\nDoqQkHaK1NTgt+5Lk+Yzv5LPJSvOWZ9TRMyNUMjz5MV6bKXOZ3S0QjFhgkJhYaFQDBqkUNy6pbx9\na4DiuDZnzlQoKlZUKFq2VCh27y7896Y6K4nf66qkCXlnkdfM/7sXp1wuZ8SIEVy9ehV9fX02btxI\ntWrVmDFjBnfv3mXlypVMmTKFOXPmUKVKldfuT9TMC4KgjnKf5nJn+B2e3XhG5Z9c+GKWEfHxUhMT\nm0J2l8zIyWDZ+WUsv7CcoW5DmfbhNEz0TQCQy7OJiVnPgwfzMTdvib39XAwNqynhjNRfwrEEkEO5\njuVUHYpyJCZKS4r+8AM0by61tXR3V3VUJYKPj9T2v2ZNVUciaBpNyDuLNJn/by/OvXv3cvjwYTZt\n2sTFixdZuHAh+/fvf7G9l5cX8fHxWFhY0LVrV7p37/5qwBowqYIglE4KhYKYH2OInBVJ9R+dIoeQ\n1AAAIABJREFU2HzXkuXLYcsWaNeu8PuPSYth6qmp/H7vd+Z/NB+v+l5oyaRqydzcp0RHLyM6eiUV\nKvTGzm4Gz56FkpzsR2TkbHbtgq+++gaZTIa5uSdly3oWPiChaDx7JrVS+e47qQPO1KlSbYim9EZU\nIYVCTJOgXJqQdxZpzfx/e3GePXuW9u3bA1Irn8DAwJe237JlC7/99hvbtm17bSIvKJ+orVMuMZ/K\no4lzKZPJsB5hTZ3DdQifcJce8ffYuV3OoEFSO/LCthmvbFKZLV23sL/XfjYEb8BjgwdnH5wFQEen\nDHZ23+DhcQuZTIdLl2qRnHwaG5vxBATArVsQGloXe/tZIpFXgiK9Po2NYexYqai7d2/43/+kp6v3\n75darZQwhZ3L542CeveGQYOUE5Mm08SfnULhFGky/99enGlpaZiamr4Ya2trIy+BP5gEQSjdTD1M\naRDUgGc3nlF2zhXOHc7Czw86dIAnTwq/fw9rD84NOsf4xuPps6cPn/t+zv3k+wDo6Vni6LgCd/dA\ndu8+TvPmFly9Cl27wv79U2nVyoXNm9cVPohilhVbCnuz6+nBwIFw4wZMngzz5kGdOrB1K+TkqDo6\nlUtJgVWroHZtGD5cer+zbJmqoxKE4lesjYpNTU1JS0t7MZbL5Wi9Rw8ob29v7OzsADA3N6d+/fp4\n/r0C3/N3pGKcv/Hzv1OXeDR9/Pzv1CUeTR57enqqVTwFHeuW0yVhYgJxO+Ko2imDX7c5M+anK7i4\nwP79njRpUrj9y2QyKidUZn2d9VzUuYjbejc66nSkT50+dGjTAUNDexo1Wkhm5i+Eha3F1RWOHXtE\nq1Zj8fIapvL5yfdYAdWuVCNqSRTZ67PBWD3iK/brs3t3/CwsICgIzy1bYMYM/D75BDp2xPPvT7zV\n4t+rmMY5OeDklEmtWqmsXl2BFi3A39+PK1fUIz4xLjljTVDkD8D+u7H+3r17OXToED4+Ply4cIG5\nc+fy22+/FWh/mlC7JAiC8G+JJxO51f8W1mOsueJclSFDZUydKlVSKKu+NyoliimnpuAf6c/CVgvp\nW7cvWjItDh/2ZcuWngAoFNo0a1aBPn1WYGnZA9nf9fbqKi89j9tDb5N+Mx2XvS4Y2huqOiT1cfEi\nfPstnD8PX34pLVNqroaLZRWh9PT3X6RNEPJLE/LOYvlJ/rwX56effoqBgQFNmzZlwoQJLF++vDgO\nL7yFJr3z1ARiPpWnJM2lRWsL3ALcSDiYgP3G65w9kcOOHdCzJ6SmKucYNmY27Oi2g197/sqqS6to\n8lMT1gSsYZPfWp7aQmAcGHl8SsDDity8O4OgIHcSEn5T219SGREZXG56GWTges5V7RJ5lV+fjRrB\nvn3wxx/SSmXVqsFXX0FsrGrjeg9vm8uwMLh8+fVfE4n866n82hSKXZEn83Z2dvz111+AlNT/+OOP\nnDt3jnPnzuHk5FTUhxcEQVALBlUMqO9XH8PqhiR2C+LoqjQsLaFBA1DmgteNbRpzYcgFRjQcwYI/\nF2DYpCK1TCE2CT5x7sWOJZdp0fgWtrYzuHdvMpcvNyM52V95ASiBIk/Btc7XsPK2wnmbM9pG2qoO\nSX3VqiW1SwoOhowMcHGBL76A8HBVR5YvCoWC9etfXs04Jwf27IHWraFZM+nUBEF4syIvs1E2mUyG\nl5cX3t7exV+zKMZiLMZirIRxrSe1CBsRRuyAWC4blGP9+pYsWQJ2dso93qSvP+VOxBHKOmaDPTz7\nqwxaGVp06jiYAb2Xcfr0KZKSTlG58i4MDasTHd0NY+OaKp8fT09Pcp/mcjbwrMqOr7HjpCQ8g4Jg\n7Vr86tWDPn3wHDxYfeL7z9jfP4jvvovGx6c9RkZG/PyzDSdP2lG9OjRvfpPmzZ/Qtm0LtYlXjEvf\nuGXLlmr7CeZzGpnMa1jIgiAIr0i/nc717tcxcTchd7QTn/XTpkkTab0gQyVVlCgUCnwP+vLZ0s+g\nNWif0mZkz5EsH7n8peYDcnk2sbGbuH9/LqamHtjZzaVMmdrKCUJQjdRUWLsWVqwANzeYMkW6za0m\n1q3bzvff7yInpx5hYfNwdJyOjs4VqlTpxbJl/agtLj9BTWhC3qn17k2Ekuz5O09BOcR8Kk9Jn0uj\nGka4X3QHBWR6B+O/M530dGmVyrt3lXMMmUwmPbOUC+wGffQ5FHaIpj5NufTw0ovttLT0sLb+gkaN\n7mJm1owrVz4iNLQ/GRn3lBNICaT216epqdTOMjwcOncGLy/48EM4ckRqzF7MMjKkcpktW2DSJGjd\nui+zZo0kM1MOyMjMlDNnziiOH+8rEvlCUvtrU1A6kcwLgiCoiLaxNjW31MT6S2vC2l1mRbfHDBsG\nTZrA3r3KOUZYRBhUBzxg66StDK02lOHuw/l096cM2DeAh6kP/4lH2xAbmwk0anQXQ0NHgoI8uH37\nC7KyHr75AIWUcCSB6JXRRbb/Us/AQGrCfvs2jBghrSbr6gq7dkFubpEfftYsqFEDLCzA2xuOH5f+\n38BAeqOZnJyJre1wkpMz/nnzKQhCgWhkmY2omRdjMRbjkjZOC0pj28fbMGtmRrVxvenVRwsPDz+G\nDYPWrd9v/yt2rSDkUQh29e0AiAyJBMC7qzcNKjfgi1VfcPDOQSb2mcjEJhO5dO7SS68/efIAcXG7\nsLU9gZWVNxERH6Kra66U81XIFfgP8YdD4LrfFbOmZmr171FixwoFnunpsHAhfhER8PnneC5YAAYG\nBfv3U4Cv73nCw43R0qpL8+aQkfHq9nfulKFp0wY4OcG5cy9/fejQqdjYlGfGjPHs3XuC48f96dOn\nrXrNlxiX+rGomS8CmlC7JAiC8D5yEnMIHRBKblIuVutcGPqVPikpsHs3WFsXzTEjkyP56uRXnI86\nz6LWi+hVu9crd0ezsmK4f38+jx/vwtp6JDY2E9DRMXvvY+am5hI6IJScJzm4/OqCfmX9wp6G8D7O\nnoWFC6Xej+PGSV1wTEze+pLdu6XnOq5fB319aUHa2rWhXz9wdy+muAWhGGlC3qml6gAE1Xr+zlNQ\nDjGfylMa51LXQpc6B+tQ7uNyRLYJYsuYRDp2lNpXnjxZuH2/aT7tzO3Y3WM3O7rtYOn5pTTd9HI9\nPYC+fmWcnFbj7h5IZuYDLl505MGDReTlpRc4jvSwdII8gtCvrE/90/U1NpEvEddns2bw229w9CgE\nB5Nh50zQkB/ZsiqV48df/5JatWDOHKm1/aNH8PvvsHx54RL5EjGXakTMZ+kjknlBEAQ1ItOSYTvV\nFucdztzxvkUfeSTbtykYMADmzgW5vGiO+6HthwQMDWCo21C67ur6Sj09gKGhPc7Om6lf35+0tEAu\nXqxOdPQPyOVZ+T6OtpE2ttNscVrjhJae+BWkagEB0H1OPZyCfsbi2QMG7evMiYnHebZyIzx48Mr2\ndepAy5ZgaamCYAVBeC1RZiMIgqCmsh5mcbPXTbRNtTFb7Ez//+liZATbt0P58kV33LSsNL49+y1r\ng9YyttFYJjaZiKHuq/0y09KCiYiYzrNnN7Gzm0XFiv3Q0tIpusCEAlEoIDpaKonJyoKuXV/dJiIC\nLl2SknRHR9DVRVpFdvly+OknqRPO5MnSLXlBKIU0Ie/UyGRePAArxmIsxqVlLM+VU/VYVeL3xPN4\nUgI7zxhz/rwnu3dDZmbRHv/nQz+zLmgdEeYRLGq9iIpPKiKTyV7Zvn59HSIipnH+fCSVKg2ia9dv\nkMm01GL+Sts4IUGPkyebcO0ahITkoqcnx91djzZtwMOjgPs7dAj278fz8GFo0gS/tm3B2VmtzleM\nxbiox+IB2CKgCe+QNImfn9+LC1coPDGfyiPm8mVP9j7hzhd3sJttR0ClygwbJmPGDBg1CvLTza8w\n83nm/hnGHhuLoa4hK9qtoKF1w1e2USgUJCWdIDz8ayAPe/v5GCZ5YuhgiEyr5LUbVNX1mZ4OoaEQ\nGQndu7/69ZQUqZf78wdTlVIOk54u3aVfuhSqV5faW7Zqlb8LLx/E97pyiflULk3IO7VUHYAgCILw\nbpbdLHE950rM2hicfEM590cePj7w+efSYp9FqbltcwKGBjDEdQif7PoEr/1er9TTy2QyLCza4e4e\ngK3tDG6v+5mAhqeJPedftMGVcHl5MHOmlLg7OUG5cjBwIBw48Pq1n8zMYPRoJde1GxnBl19Kq5kN\nGCAdwMMD9uwpuoc4BEHIN3FnXhAEQYPkpecRNjKM1EupVNvpwrQ1xvj5ga+vdDe2qKVlpbHw7ELW\nB61n7AdjmdB4wkv19PJcORFfR/Dkl8dYrb/PI5NpGBo6Ym8/D1PTV+/ol3b/rmtv1Qr09F7dZv58\n6YZ47dpSQq+rW/xxvkQuh4MHpbaWKSnw1VfQt+/rgxcEDacJeadI5gVBEDRQ7E+xhE8Jx3G1I8cy\nKzBhAnz3nXTjtDhEJEUw+eRkLj28xOLWi/nM5TNyE3O52esmAM4/O6NXXg+5PJvY2E3cvz8XU9NG\n2NvPxdjYpXiCVFNbt8L583DtmpTEGxpKifrWrVCpkqqjKwCFAk6flpL627dhwgQYMgSMjVUdmSAo\njSbknaLMppR7/oCHoBxiPpVHzOXbVRpcibon6hI+NZzGQWGcPC5n/nwYNgwyM1/dXtnzaV/Wnl97\n/sq2T7ex6Nwimvk048LQC5RxK0Odo3XQKy/dpdXS0sPa+gsaNbqLmVlTQkI+IjS0PxkZ95QaT3FS\nKBT06TPqjb/g09MhKAgSEl7/+vh4qTnMvHkQFiY1j/n9dw1L5EGqmf/oIyn4PXvgzz/B3l5qRJ+Y\nmO/diO915RLzWfpoZA8xb29v0c1GSeOQkBC1ikfTx2I+xbg4x0EpQeSuzMV4gzG5Iy6zaFoCKzfr\n0aSJJ7/+ClFRxRNPwNAAtlzZQo8LPXA1cWVTxiYqm1R+aXttbUPu3XMnL28ThoZBBAU1Ijy8CVZW\n/WnbtqdazGd+x/HxmRw4IGPu3GU0b+6OTObJH3/A6dNPiIgwJj7eCCcnGDo0mNq1U195/fjx/4xv\n3FD9+Shl3LAhfqNGQZcuePr5QfXq+LVpAz174tmjx1tf/5xanY8Gj59Tl3g0fawJRJmNIAiChlMo\nFEQtiSJqWRQ1tziz844Fc+dKzynm5sLs2QpgCd98M+nv1pLw9+8rpUrLSmPBnwvYELyBcR+MY3zj\n8a/tTw+Qk5PAgweLiY3diJWVN1WrTkFPT71XIlq3bjvff7+LnJx6hIXNw9FxOrq6V3B374Wtbb8X\nHWRe9GsvzaKipLqvrVulp3cnT5YmRhA0jCbknSKZFwRBKCGS/ZO52ecmlYdVJraNLb16y/j8c1iy\n5BhwHF/f9nTv3q7I4whPCmfy75MJjAlkUetFfObyGbI3tDHMyorh/v35PH68C2vrkdjYTEBHx6zI\nYywIhQL8/ODhQwX6+seYMOEMUVELsbGZyrJlLejevd0bz6/Ui4+HVatgzRqpJGfKFHB1lSbUzw9m\nz5a2mzlT+m9RvdMUhPekCXmnlqoDEFRLkz5G0gRiPpVHzGXBmbcwxz3QnaQ/kjCac5VBH/3I6hUd\n0cIf6MKkYSdxrNCW5RPWFeo4qQGp3Bp4642/4BzKOuD7mS9bum7h23Pf8qHPhwTGBL52W339yjg5\nrcbdPZDMzAdcvOjIgweLyctLL1SMypCdDdu2gZsbjBgBWloyZDIZycmZ2NoOJzk5A5lMJhL5tylf\nXkrYw8OldpYffwwdOkj19jNnogCGA4qZM2HWLJHIK4H42Vn6iGReEAShBNGvpE+9U/UoU68MbU7V\nY/XMwchRADIy9LRZuGY8Y5cOe+/9x/rEcq3TNcp1KffOJLaFXQsChwYysP5AOv/cmYEHBhKTFvPa\nbQ0N7XF23kz9+v6kpQVw8WJ1Hj5cjVye/d6xvi+FAhYtkp7l3LxZag154wb06QNhYVH4+LTHx6cX\nPj4dCAuLKvb4NJKJidTtJjwcunWTut40bcpxIAE4sXevqiMUBI0lymwEQRBKqPgD8aztv41ZaffQ\nIY0szJk5syOzZhW81EaeLefuuLsknUqi9r7aGDsXrP1galbqi3r68R+Mf2s9PUBaWjAREdNJTw/F\n1nYmFSv2Q0ur+Ho2LFkCbdpA/frFdshSZfuPP7Jr/nzqPXzIPGB6pUpcKVuWXqNH02/4cFWHJwgv\naELeqZF35r29vV98jOTn5/fSR0piLMZiLMZiLFnqv5TVrMODCI7gRfUykcyZ/RvDB/sXaH85STmE\nfBRCTEgMGUszXiTyBYnHVN+U9jrtWeW8isuPLuO82pmZPjM5ffr0a7c3MXEjMXEy8fHjefRoE4GB\nddi3bxanT/9RLPM3aRIkJ6vXv2dJGvf94gs+HDKECEAGyOPjaZ6ainVGhlrEJ8Zi/N+xOhN35ks5\nPz+/F22YhMIT86k8Yi4LT6FQcNj3MCs/28fH1CVQPxCP6YNYur4lI0fKmDxZKl1+F3munMc7H1Ox\nX0VkWsqpD/eL9GPssbGY6Juwot0K3Cu7v/U8kpJOEB7+NSDH3n4eFhYdClWrnpsrtUZ/8EBK3Asc\nv7g+C+2Yry/He/YkGrA2MaGDtzftDh2SmvAvXAh166o6RI0krk3l0oS8UyPvzAuCIAjv5u8v4+df\naxBDe5aTRHBOF44sMmLBpGx27IAvv4S8vHfvR0tHC6sBVkpL5AE87TwJGhaEVz0vPv75YwYeGEhs\nWuxrt5XJZFhYtMPdPQBb2+ncuzeJy5c/JDn5TIGPm5IidUysVg1WrwZn58KeifC+osLCaA+MADr4\n+BBVuTLcugXt2kHbttC/P0REqDpMQVB74s68IAhCCbZ64WqeTXtGQxqS7pvOtW3XaH6xOVW2uTBw\noRkmJrBzJxgZqS7G5/X0G4M3Mr6xVE9voGPwxu0Vijzi4nYSGTkTQ0NH7O3nY2ra4J3HmToV1q+X\ncsVx46BhQ2WehfBenn+68t/f62lpsGwZfP899O0LX38NFSsWf3xCqacJeadI5gVBEEqoJL8kkv2S\nX/l7LX0topdFY7fKia8OW3LvHhw6JHURzIzKREtPC72KesUe773Ee0w+OZng2GAWt15Mj1o93lpK\nI5dnExu7ifv352Fq6oG9/VyMjV3euP3PP0PTplC1alFELxSIn5/057/+22f+8WOpndD27TBqlNQR\nx9S0eGIUBDQj7xTJfCknauuUS8yn8oi5VK7/zmdacBrXulzDeqwNP8ZXYe9eGftnJ5M8/ibVllWj\nYm/V3QU9HXGaccfHYapvyor2K3Cr5PbW7fPyMoiJWcODB4uxsGiHnd0sDA0dijRGcX0qT77mMjIS\nvvkGjh+XPmL53/9AX784wtM44tpULk3IO0XNvCAIQilk4maC23k3Hm95xMDUO8yuG0VYvxvIptVU\naSIP0NK+JUHDghhQbwCddnZi0IFBb6ynB9DWNsTGZgIuLmH88ktv+vU7xZ07/yMr62ExRi0UKTs7\n2LoVfv8dTp2CGjVgy5b8PfQhCCWcuDMvCIJQimXFZhHkHkTeszwS5rgydF4ZNm+GTp1UHZkkNSuV\n+Wfm89Pln5jQeALjGo97pZ4+Ohp++AE2bpQqNEaPTsHaegGxsRuxshpI1apT0NMrr5oTEIrGn3/C\nlCmQmgoLFkgry4qVeIUioAl5p0Ym815eXnh7e+Pp6fmiB+jzj5TEWIzFWIzFuADjfWD52BItfS3O\nnDtD5kAHvl3VhjlzwNFRDeL7e3wv8R7eK7wJSwzjhxE/0N25O/7+/qxc6Yi/vzX9+0OjRheoXDnz\nxetPnPAlLm479vZnsbYeSXh4Q7S1y6jF+YixEsanT8P583ju3Anm5vh9/jnUqaM+8YlxiRi3bNlS\nJPPKpgnvkDSJn5/fiwtXKDwxn8oj5lK53jSfCrlCWrUHeLDwATHrYjBcXZfuY4zp2xdmz1avG57P\n6+nNDMxY3m45qbfdqF8fzM3f/JqMjAgiI2eTmHgEG5uJWFuPQlu7cO17xPWpPIWey7w86QHZb76R\netMvWAB16igtPk0jrk3l0oS8U0vVAQiCIAiqI9OSIZNJf2yn2eLwrQPPBoVwZGESx47BoEGQk6Pq\nKP/xvJ6+X51+dNrZiW0pg8nUefTW1xga2uPsvJn69f1JSwvg4kVHHj5cjVyeXUxRC0VKWxu8vODO\nHWjVClq3hgEDpIdmBaEUEHfmBUEQSjiFQsGCqQuYtnBavlZNTf4zmRs9b2A1sxpjj1iRnQ2+vmBi\nUgzB/sejR7BmDVy+LLXP/LeUzBTm/zmfTZc3vbGe/nXS0oKJiJhOenootrYzqVixH1paOkV0BkKx\nS02VVgb74Qfo10/qUV+hgqqjEjSUJuSd4s68IAhCCffbnt+4+sNV1tRfQ07iu2+zm39ojqu/K0++\ni+S7OuHY2ipo3hxi39xQRumuX4fBg6FWLYiPl3Kz/zIzMGNxm8VcGHKBSzGXqLW6Fntu7nnnL14T\nEzfq1j1CzZrbePRoE4GBdXj82BeFQl5EZyMUK1NTqT7s5k1pMSpnZ5g1S1qIShBKIJHMl3LPH/AQ\nlEPMp/KIuSy8zes208qlFfvG7+OLZ18Q+CiQ9h+2Z/O6ze98rVENI9zOu/H0dDJjnobSvaucxo0h\nNLTo4x46FNq2BQcHqXJizRpwcnrz9tUtqrPv831s7LKROWfm4LnFk8uxl995HHPzZtSv70/16it4\n8OBbgoIakJBwNF934cT1qTxFNpcVK0oryAYEwN274OgojbOyiuZ4akJcm6WPSOYFQRBKKK9hXowa\nPYrMh5nIkCHTlzFu9ji8hnnl6/V6lnrU+6Me5CjodOoKMybl4ukpdQUsSuPGQUSEVB1RvgAdJT+y\n/4jgYcH0rdOXDjs6MPjAYB49fXs9vUwmw8KiHe7uAdjaTufevYmEhDQnOflMIc9CUBsODtIDsseP\nS39q1oRt20SPeqHEEDXzgiAIJZQ8R86aums4E3EGo2pGZERlMMBnAJ26F6yJvEKuIHxKOPEH43k8\npT6DJunz44/Qo0fh4svLk55dLAopmSnMOzMPnxAfJjWZxJgPxuSrnl6hyCMubieRkTMxNHTC3n4e\npqYNiiZIQTXOnJF61D99KnW+6dRJvVo2CWpFE/JOcWdeEAShhEr2TyZOEYfXdi98rvswwGcA98Pu\nF3g/Mi0Z1RZXo8qYKpSbGsTe79IYOxZWrHi/uG7dguHDwcNDKmkuCmYGZixpu4QLQy5wPvp8vuvp\nZTJtrKz64+Fxi/Llu3L9eleuX+/Os2c3SUryIyJiFqdPyxg+XEZ4+EwiImaRlORXNCchFI3mzeHc\nOZg7F7766p+xIGgokcyXcqK2TrnEfCqPmMvCs2htwZybc+jUoxP+/v506t6JEVNGvPf+rP9nTY2f\naqA14SoHpyewfj2MHw/yfDw3qlDA6dPQuTO0aAFWVnDkSNHfEK1uUZ39vfazofMGZvvPpuWWlvmq\np9fS0sPa+gsaNQrDzKwJISEtefRoE1ZWAwgIkN6QhIbWxd5+FmXLehbtSZRwKvlel8ngk0/g6lXp\nSes+faBLF+nJaw0nfnaWPiKZFwRBKMFkWsrNlst1LEfdE3XJnHeb3b0fEhiooHdvyMx8++u8vGDE\nCCmZj4yUmo1UrKjU0N6qlUMrLg+/TO/avemwowNDDw4l7mncO1+nrW2Ijc0EGjUK48iRJFq1cuTq\nVejaFfbvn0qrVi5s3ryuGM5AKBLa2uDtDbdvQ8uWUp96b2+4X/BPsARBVTQymff29n7xztPPz++l\nd6FiXLDx879Tl3g0ffz879QlHk0ee3p6qlU8mj5W5nyauJrgdt6Ncz6H8DbdgVyuoG1bOHjwza9f\ntAhWr/bDyckPQ0PVzMefZ/6kxtMa3B51GzMDM5wmODFs1TCycrPe+XodHVPc3MbTseME5HIIuA4R\nEZF8+GFTvLyGqeR8Ssr4+WqlKo3HwAA/V1f8fvoJqlYFNzf8evTAb/9+lc9PQcdqMZ8lcFzUFAoF\nKSkppKWlsXXrVpKSkvL9WvEArCAIgvBeclNzudHzBgptGZsdanPiDy1+/RVcXFQdWf6EJYQx6fdJ\nXHt8jSVtlvBpzU/fuajW4cO+zF/ck8BMaGioQ8+u5nh61sLGZgLlyn2MTKaR98iE/4qLk2rqf/4Z\nxoyR6snKlFF1VIIKFFfe+fnnn/Pxxx/z119/oVAoiIuLY9++ffl6rfipU8oV57vO0kDMp/KIuSy4\nR1sfkXw2+bVfK4r51DHVoc7hOhhU1qfOiTtoK+TUrw/BwUo/VJFwLOfI/l77Wf/xemb6zeSjrR8R\n8ijkjduv81nH0MkjuSuD3I5wX9+Sb9fD6E3xfHPyf0zbU4kNZ4fjF/E7YQlhxXgmmk0tv9crVpRW\nkL10SSrBcXSUxtnZqo7sndRyPoV3iomJoX///oSGhrJ27VrSCrDImVi/WhAEoQRIDUzl3oR7uJ5z\nLbZj5ubCnr1aLLvmRFxCLj0UkYyZXYn27Q3Zvl1a+EkTPK+n/yn4J9pvb09np87M+2geFYwrcP3x\ndfaE7uG3sN/4Y8AfWFhYMGrVZyADbV0dFsxdSHzleOLT44lJvsbFmweJD9yEhbEtJ7zOoaf38oMB\nsWmxzPGfg6WxJZZGlpQ3Ko+lsSXWJtY4WzqraAaEN6pWDXbsgJAQmDYNli2T7tj37g1a4n6ooDw5\nOTns3bsXFxcXnjx5UqBkXpTZCIIgaLicxByC3INwWOJAhR4Viu243t4QHi5VIHTuDPG/xnF39F2S\np9Rh0CJTFi+WHnzVJMmZyXx55Et8Q30po1cGQx1DetTqQTfnbjiXyWb33rVM3nqASsamxD5NZYnX\nJ/TqPuKljjbp6XeIjl7O48e7sbTsTpUq4zE2lhL1hPQEdl3fRXx6PE/Sn/Ak/Qnx6fFYGlmyq8eu\nV+KJSIpg7pm5LyX+lkaWVDWrSp2KdYprWoTn/P2ldpYZGbBwIXToIHrUl3DFlXfu3buV8nbOAAAg\nAElEQVSXXbt2sWzZMtavX4+Hhwcff/xxvl4rknlBEAQNppAruNb5GkY1jKi+rHqxHjs9HYyMXv67\n5D+TudHzBnkjnfDaZMmQIdINTU3Kd776/SuSM5O5lXCLqJQovmv7HV1rdsX/vj/zls/jVOIpqAY9\n9XuSGJfI9LHT8bTzfGU/2dnxxMT8yMOHqzExccfGZgLm5i3fWZf/b0+ePeHA7QNS8v/syYs3AFVM\nqrChy4ZXtr/55Cbz/5yPpdHLd/7tze1xrVR8n9qUaAoFHDggXdiWlvDtt9C4saqjEoqIJuSdIpkv\n5f795LtQeGI+lUfMZf7cn3+fhKMJ1D9dHy3dN3/sX5j5vH8fbG3zv336nXSudrwKnSrxvzNV8fCQ\nsXo16KhRYWdOXg4JGQlYlbF663Ynw08y7vg4yhuVZ0W7FdSzqodslgwOgTxQnq/EPC8vk7i47URH\nL0NLywAbmwlYWn6Glpausk7nhcfPHnP87nEp6X/25MUnANXKVuO7dt+9sn1QTBDfnvuW8oblXyr9\ncSrnhHtld6XH918KhYK+Q/qyY+OOAr3JUQu5ubBtG8ycCW5uMH++Wjz9LX52Kldx5Z0LFixg8eLF\nGP7d7ksmkxETE5Ov16rRj1ZBEAShoEybmGLlbfXWRP595OXBwYNSifCTJ3DtGujmM/c0cjLC7bwb\n1z+5zvrqGXwdXoNPP5WxaxcYGys1zALJzM3k93u/syd0D4fuHGJg/YEsbbv0ra9p7dCay8MvszF4\nI+22t6NLjS5wF8iAvYf30r1z93ceV1vbgMqVh1Cp0iASE48RFfUd4eFTsLYeTaVKQ9HVNVfSGUIF\n4wr0r9c/39tXMa1Cz1o9XyT+txNuczbqLM7lnV+bzJ99cJZF5xa9uPNvaSwl/7Usa+Fh7VHgePcc\n2sOB2wfyPZdqRUcHBg6U6udXr5b61HfqJC2iULWqqqMTNMyuXbuIiYnB6L8fd+aDuDMvCIIgvPD0\nKWzeDCtWQPnyMGECfPrp+91Vz8vI45bXLZ7FZLO6aj1C72px+DBUKL6yfgDinsYx5tgYjt09Rj2r\nenR37s6nNT/FxsymQPtZsW4Fc3+cS6JpInwE1S5XQz9en9FDRjN84PAC7Sst7TJRUd+RmHgEKysv\nrK3HYGhoV6B9qELc0zguRF94Uev/vPSnbsW6TGwy8ZXtj989znfnv3v5gV8jSyL8Izi09xA5FXII\nqxeG4xVHdB/rvtdcqo2UFFiyBH78UXpYZNo06ZtI0GjFlXd27dqVvXv3ovUeD1aLZF4QBEF4YeRI\nqcX2+PFSGXBhKx8UcgXhU8N5si+eA+3c+PWoLkePSp3+iktWbhZbrmzhkxqfULHM+y87q1Ao8D3o\ny2dLP4PWIDslo2u7rqwbuw5LY8v32mdmZjQPH35PbOwmypZthY3NBExNC36HW13FPY3j8qPLL9f8\nP3uCWyU3yseUZ8L6CUR5RGFzyYZlw5fx1OYpU05NwVjPGGNd4xf//aTGJ3zZ6MtX9n/j8Q3OR59/\naVtjPWOsTawL/GZNaWJjpY43v/wCY8dKf0SPeo1VXHlnhw4dePDgAXXq1EEmkyGTydi5c2e+XiuS\n+VJO1NYpl5hP5RFzqVz5nU+5vGg67j1c+5D7s+8T6FWfhVuM2LcPPvhAeft//OwxB24doHut7lgY\nWihvx//he9CXngt7QgoYlzemsWdjgoyDGOw6mIlNJr73m4Xc3DRiY38iOnoFBgZV/16EqnOJXoTK\n96Avg74bhIWuBYk5ifhM9KFTh04kZSTxLOcZz7KfvfhvxTIVqVux7iv7OHP/DFtCtkjb/es1XZy6\n8HXzr1/Z3ueyD7P9Z7/yZqFrza4McRvyyvY3n9wkODb4lTcLlcpUeve/9d27MGMG+PnB9OkwdCjo\n6b3vdOWLRj+DoKaKK+/08/N75d+sRYsW+XqtqJkXBEHQINnx2eiVL1hCoFAoWL/elxYtWiCTyZDL\nISAAGjV6dduiap1t/YU1BrYGKLwus3SwC507m7NxI3zyyfvvMzo1mn2h+9gTuofLjy7Tvnp7Wju0\nLtJkPiwiDKoD2rCl+xbCIsLY5LWJxecW47zamQH1BjCpySSsTa0LtF8dHRNsbMZibT2K+Pi93L8/\nn3v3JlGlyjisrLzQ1i54Ha26C4sIw2eiDxZlLP7P3pnHx3R9Afz7JotsSEJsSSR2YkvslAq1laLE\nWi3RWlptqfJrUbSWVqmllKqloqpFG5SqvTK22Il9CZKIJRLZZF9m7u+P1wSZCZNksnrfz2c+kztz\n7n3n3bw379x7zz2HqPgoAoMCsTC1oHLpyga38arLq7zq8qrB8gPqD8DT1VNnsJDdLP69x/fYdXPX\nM7IJaQkMcBugd7Cw5px8LWQa/m9YY92hPm8eWsHbGTHqBw3KvNGuRlzlcsRlncFCBesKubqOi/Ue\nhJccDw8PZs+ezeXLl6lTpw7Tpk0zuK4yM6+goKBQTEi4lkCAZwDNLzTHvILhBr2v727efXcPK1Z0\n4/HjrixaJK/6HzkCFhb5qLAe4gLiuNTzEo/6VmP0nxWZNk3igw9y3s7sQ7NZeGwhPev0xKueF52r\nd8bSzNL4CutBmiHPnokvn30WPYh7wHz/+fgE+DCowSAmtZ1E1bK52wgphCA29gihoQt4/NifKlVG\n4+j4kU4SKoWiRUxyDPfj7usY/9XtqtPsehxMmgQpKXKM+m7d2BH4Dz4BPjrywxoPY0q7KTrt/3z2\nZ344+YPOyoLVFSuO7jqqswehT78+uLZ3xdzEHDOVGWYmZpibmFO7XG3qlq+r035kYiQxyTGZcmYq\n+d3C1AIzE+NHXyrqCCFQWakQSflvd3p5edG+fXvatWvHwYMHOXDgANu3bzeormLMKygoKBQD0uPT\nOdvyLE7jnagyoopBdVasWM+SJRtJSWnMrVuzUammYmV1nlGjBjF//tuFFvs9+W4yF3tcJLZBecac\ncsXLS+Lrr3O2KhCZGEnpUqUxN8lft4WnUQerUQerdT73dPV8Js58eEI4C48tZNXZVfSt25fJ7SZT\n3a56ro8rJ6H6nvDwjZQv3wdn50+xti78EIgKuUAI2LpV3hxbqZIcoz4H/maPEh8RGhuqs7JQp1wd\nQs6E6OxBsKprxearm0nTppGqSSVNm0aaJo2+9fri7e6t0/6K0yuY5z9PltU8qTO+1XhmdpipI7/k\nxBK+8/9OZ7Dwnsd7jGk+Rkd+27VtbL66WUe+U/VOdKvZTUc+ICyAcw/O6QwuaperTa1yuhtvopKi\niE2OlWVNzDLlS5mWwlSVc2cU3+2+9P+qP+Js/tudnp6eqNXqzHLbtm05cuSIQXUVY/4lR/FLNi5K\nfxoPpS+fIITg6ttXUZmrqLOmjsG+sEIIfH13M3LkIWJj51Cp0mR++KE9Xl5dC92fNv1xOpcHXCZG\nY8rnsW7UrC2xZo3sUiyE4FzYOTZf2UxEYgQre64sVF31Ycj1GZkYyeITi/nx1I/0qN2DKW2nUKd8\nnVwfU05C9RP37y/DxsbjvyRUHQv9f5lXXsp7PT0dfvkFvvoKmjWDb76BevXy1KS+PQj57WoTlxJH\ndHL0M4Z/miaNCtYV9LouXXx4kbMPzj4ZXGjSSNOm0bxKc9q76vqH77ixQx6MZGm/v1t/hrnrppde\ndnIZ3/l/p9P+xNYTmdFhho784uOLWXR8ke5g4V5tzu07l7nSIb7Kf7uzVatWbN26lcqVKxMWFkbf\nvn3x9/c3qG6x9Jn39vbG29v7mVFMxg+BUs5ZOSAgoEjpU9zLSn8q5fwo17pSi4RLCSTMTSDsYJjB\n9Q8ePMiVK1fQapNxcRlNRIQZV65cpl+/boV+fqZlTImcGMndRXeZn5LC3EcNadh9BW5ehwhIPYWJ\nZEJzm+a0L//kAV9U/h+Gli+evEhHqSOfjv2UH078QIupLWhWuRlLPlhC/Qr1c9yev/8loC3t2k0k\nPPx3Nm58D5XKlN69v6RChYEcOuRfpM7f0HIGRUWfAimbmqKuUQNWr8bz4kVo3x5106YwfDieAwbk\nqv09/+5hYreJtGvVjqj4KPb8u4dypcsVjfN9qjzMc5jB8jbY4NPbx2D5+tQn+JNgg+VrptdE7a0m\nVZPK0eNHSdem07hJYypaV8TH3IflB5ZDAY2VZ82axSuvvEKZMmV4/Pgxq1bpZnjODmVmXkFBQaEI\no0nWcLblWepvro9VzZxvgpwzZxW1a1elb98ubNmyl8DAUCZN0o3aUVgIIbgz9w5BP95h8KAv0IZ0\nYP0XXnT6LzxbSSIuJY4fT/3IouOLaFu1LVNfnYp7JfdctyeEyExClZh4DSenj6lcebRRk1ApFBAx\nMTBvHqxYISeimjwZypUrbK1eajJWOuLuxSFuFpzd+ejRI8rnMD+BYsy/xAgh+G7yZP43Z06Je2gq\nKJQkhEYgmZSMezQlPQWN0GBl9uzAJHxTODc+CmRfLw989lmxcyc0aFBISuYzCakJrDyzku/8v6NZ\nlWZMe3UazR2b56nNuLgA7t5dSGTkDipWHIqT0zgsLasZSWOFAuPBA5g5E/78E8aPl2PUF2ba5JeY\nOYvnULt6bfr16pevdueHH37IsmXLaN269TOfS5JksJtNsTPm7SSJKK1WMT6NwG5fX/4ZNow31q2j\nq5cSwsoYqNXqzCU8hbyh9GXuEAISEnRz1BRmfyamJbL75m42X93MzsCdrOq5in5u/XTkYo7EcLnf\nZc694cbM7Xb88QcU1UvAGP2ZlJbEz+d+Zu7RuTSo0IBpr06jjXObPLUpJ6H6gQcPfsbOruN/Saj0\nxCAtQij3uh4CA+UY9YcOye8jRoCZYdFklP40Lvk9ifzw4UMqVqxIYGAgZk/9j6Ojo/Hw8DCojWLn\nM98X2LtlS8EZn0KARiOHWVCpdL9/9Eh+cmo08oYWjUZ+Va0KZcroyp87B+HhuvKvvAKV9cTW3b4d\ngoJ05QcNgpo1deV//BEuXtSVnzgR3OXl3PUrVrBxyRIap6WxJDGRqcOG8cO77zKofn3edneXY9WN\nGgV1dcNWcfgwREXJMk+/qleH0qVz2LklCyEEvitXZsbyVlAoDL79Fk6fhs2bC1sTOH3/NHOOzGH/\n7f00r9Icr3pezO88P9s44rZtbfE47IFp94ss7OzMgAGVWbJEYtCgAla8gLA0s+SjFh8xsslI1gas\nZciWIVS3q870V6fr3QxoCBYWTtSoMRcXl6mEha3hypVBmJs74uw8gfLleyFJJkY+C4V8oVYt2LgR\nzpyRI98sWACzZ8OAAfmXDEJBByEE+e20ptVquX79OsOGDWPdunUAaDQaRo8ezcmTJw1qo9jNzAtJ\nYqqNDedVKgZVr87bLi6ysTptGrTQkwL7k0/kkW1W43blSujQQVe+Xz/YufOJrFYLJibw99/w+uu6\n8u+8AwcPgqmpLGdiIv/944/wqp5EFlOmyE/arPJTpkCTJrryP/4I167pyg8dCrVr68r//TeEhurK\nd+gAjnISEyEEu319OTRhAnNCQ5lcvjzt33qLro0aIaWkQHIy9OkD1fQs0c6eLWebSU5+9vXjj/KA\nJCs9e8oDgKzG/08/6Q/HtWQJ3LqlK+/lJQ+QsnL1KiQl6cqXKSOfdwGy29eXPe++SzcfH2WlQ6FQ\nWLEC5s6V48dXMSx6Zb5yPuw8Zx+cpVedXpSzMtz/NzUilUtvXuJOmbKMvVSdseMkJkyg0EJpFhRp\nmjTWX1jPN0e+obJNZaa9Oo1O1TvlaXJAq03n0aOthIYuID09EienT6hUyRsTk6LhuqG4exrIgQNy\njPr0dDlGfZcuz94QajWo1YgZM/gO+N/06XJ/enoW3eWtYsBuX1/+7N+fn/PRVN66dStLliwhICAA\n9/8mXVUqFW3atGHWrFkGtVHsjHkkicn29rQfOpSubdogZRitLVtCRT3JNG7cgLi4J0ZthoHr6Kjf\nDy0pSZ6Nz5BVqUrkEyTD8JScndGGhvJ6fhmgWY3+jFf16vpXLjJWIrLKv/8+1NET0u2jj+DoUV35\nrVv1/4ANHSqvjmQ1/r/+Ghrppgrnt99kH8as8u3bg4MD8OxKx+zAQKZWr855MzMGjRnD2x98IF9L\nJfAayk9e5gd88p1kHv72EJfJLjmq98cfsovtwYP6F+3yi/tx9zl65yj96/c3aruaZA3Xhl4jNFgw\nMb4+HTtJLFok/yyXdNK16Wy6tInZh2dTtlRZpr06je61uufpXhBC8PixP6GhC4iNPUzlynISqlKl\nKhlR85yjTILkACHkJbcvvpBtmDlzdNI475Yk9gDdfH2Lfn++yPMhNFT2fEhPh7S0J++NGun6EQLs\n3i17PmSVHzxYv324YIFsb2SVnz2b9fv2PfNclwrAVP7nn3/o0aNHruoWO2O+qyRRr3Tp/DM+XxJW\nzZlD1dq1Mbe3JzUqitDAQEZMmlTYauU/d+7IUQOyGv+tW2ca58+wciVcv64rP2NG5u48nZUOlYr2\nFhZ0FQIp4wfi4EH9KzXe3nD+vBxc29wcSpWS3+fN07/7b/ly+Qcuq7yXl/6p2LNn5R/DrPJOTgWf\n+jMHvKz7ObQpWs69eg6Hfg5U/Z/hmUP37JHHqfv26R+TCiH4eMgQfvjtN6MMjoJjgtlydQubr27m\nSsQVetbuiU9vH0xUxrW0hVZwe8ptgnyjmVXeg3JVTPjtN7AsmESv2etl5P7MDo1Ww+arm5l9aDZm\nJmZMe3Uaver0QiXlzc0iMTHwvyRUGyhf/k2cnD7FxiYfdhunpz/7ylght7NjvY/Ps5MgtWpxXggG\nDRnC2wMGyIZeBtWqgZWeSE63bkFiovz30/I1a+qXv379ifzT1K6tf3Lv6lX59zMrdevqNyYvX4b4\neF196tfX74Z64YJ++UaN9MsHBMiTkxoN/PMPrF0Lbm6wYAHrz5xh45IlNLpyha+BL2rV4kJaGoO6\ndePtTp2eNVbfeCP7511IiK5x+/nn4OqqKz9uHFy58qxsWhqsW6f/+dWpE5w8+Wz7KhUcPw7N9WwA\nf/NN+X9gZiZPimW8r1mjPyb/F1/Iz/is8hMm6F/Z//13iIzUle/WDWFv/8xznQIwlY8dO4aPjw/p\n6elotVoePHjAnj17DKpb7Ix5SZLY7ev78hif+YyyUcY4ZMwuJdrbYxkV9exgU6uV3/XNPNy+DbGx\ncnrv1NQnr5Yt9Ycl+/13CA7WlR83Tv907Nix8krE07KpqbBhg5yoJCsdOshuVFmN/02b9LuBTZok\nP1Az5DNe48fLqy9Z+esvec9FVvnWreUHfNZVjmrVOG9iwqBhw3h70CB5hUOS5IGLvsHIw4fy+WUY\nWBny5cvLx8lKdLT8QMkqn52bVkKC/P/MKl+qlP4p4/R0+SGQVT7jlYUbH90g9V4q9bfUz5GROGOG\n/JzU5+kGxh0c9d7YG/9Qf3rX6Y1XPS9eq/5avmdhvb/iPje+DGFZo6bcTzBn+/bCjdqn059a7bPG\nqqWl/usnJOTJTOPT8g0a6DfeDhyAiAi06WmcCz3Fjqt/QboG91HTeaPde7qDp1WrZGMma/uffqrX\nGNN89gkp5/eSnHAbU0pjaVYVU6ks0tKlspGYlQED5AmCjHYzXrt3Q9OmuvJt2sjyGSvdpqbya9cu\nhIfHs5Mgzs60d3Kia3T0k2s/433DBv2j1MGD4dKlJ+UM+fXr9cu/886z8hmsWwcNG+p+PmyYbKBn\nZe1a/cbq8OGycZtVn59/lg36rIwYoV9+5Ur98qNGycZtBkLA/fsQHY3o25fdzZuz54MP+B74pFw5\nurVsSVeVCsnM7FmDdfp0/b/Pq1bJv6FZjVsvL/0z2/7+8vWcVb5uXf2Do9hY+T1DztS0SPv/ZzzX\nr8TFsacATGV3d3c+//xzfH19adiwIVqtlpkzZxpUt1ga88VMZYWXgIyVji59+7J3y5biO9jMavRn\nvBwd9U+HHjwoL2tmlc9upeDbb2XXt6zy8+eDm5vuKoelJe1Ll6arjY2ctyPj3vf11T+46N1b3jCW\nIZshv327/sHL66/Lg5es8rt369+D89prcOKErryfn85yNwBt28qzTlnljx6VBzBP8fD3hwS9d4Sm\nKcMxkxKeHQAcOqQjD8iDrxMndAcL+/dDy5a6gyMrK84nJzPI3Jy3nx4M7dmj/3w7d37SP/9xq6wG\nl017MG2lJ+qKHnkA9u7V336nTvrl9+3TkY/cFYmqd1csxA00WnniVZUx3tm/P/v/l772nyevb8NZ\ndv1pYSH3J/B2hnFiagr//qu//QEDZGMyq3G7apV+43DSJHnA/5+cMDEhNPEBn7iHcbVMClPbTWVg\ng4FP0tSvWgVhYbrt9++vP8DCgQMQG4tWJYiJO0x41FYwNcGu0/9wqPkuKlWWQVpwsDyj+vS5mpqC\nra3BkVaepsDcPUs60dGsHzyYlXv2UB+wAJIkiauOjoycOpW3R48ubA2LJRnP9W798jc0ZQadOnVi\n//79eHt7s3btWrp3787OnTsNqqsY8woKCkWKl/EBn3A5gQDPABrvaYBNY5snRn/Ge8b+nawkJ8uz\npBmyGfL/zQzrDI6cnGg/ezZde/Z8dua/dGkwNUUrtJy8d5ItV7fg5uCGdw2vJ+0/jY2N/pnnDBcA\nQ+Xj4/XLW1vrlY8/FsaVfpfw86jJz6ft2LRJwsMD2bLPbiVFX/vPk89YScsqb2Ki25/OzrRfsICu\n/foV6N4OIQT7b+9n1qFZPIh/wJS2U3i70duYmeTcoM7abnT0XkJD55OQcAVHx4+pUmU0ZmZ2RtL8\nWUrMJEgRQAjB//r04eG2bawDhpYqRaVu3Zi3detLt+/I2BSU3dm1a1cWLFjArFmzmDFjBv379+fi\nxYsG1S12oSkVjIviZmNclP7MO6GBgXTz8XlmP0dJx6y8GXXX1cWmSdmcVXzBvgdJkpAkieSYGEa7\nuGAZFYVkY4Nkb58po9FqOHznMFuubmHL1S2ULlUar3petHRsmfNwszmV1+d3/Dzx1pVodMIWqcdF\nbNzN6dKvMuvWSXoDjQE5T7bzAnm9/alSFbixJEkSnWt0pnONzhwMPsjMQzOZeWgmk16ZhLe7N6VM\nS+W6XXv7rtjbdyU+/jyhoQs5caIGFSu+jZPTJ1ha6nHNyAMjJ08G5N/Nkj5gz28kSaLT22+zZ9s2\nBgCOQtBp/36kQYNkn3F9K0UKRYqFCxdy+fJlPv74Y4YMGcK7775rcF3FmFdQUChSvIwPePOK5pR7\n3TAn8JgYeQL5v0izLyQ0MJCua9awbucW+nTvqzM4Ohd2jvF7xuNVz4t97+yjnoOejWVFCAsnCzwO\ne2A+4DILayYy3LsGX38j8d57BXP8ojbYbO/ann9d/8U/1J9Zh2Yx+/BsPn/lc0Y0GYGFae43udvY\nNKZevV9ISbnHvXtLOXOmBXZ2HXBymkDZsnrCCisUOqGBgXQDzIHU338n9NIlKFtWdu9ydpbzzfTs\nWaT91F9mfv75ZxYuXAjAmQx3UQNR3GwUFBQUigmJiXJ46W7dYOpUw+v5bvfl3QXv4jPRB6+eJWOA\npE3TEvhRIJcOpTIxsQHDhkt8+aUSBfbkvZPMPjSb0/dP8782/2N0s9FYmemJ7JJD0tPjCQvz4e7d\nRZibV/4vCVVvJQlVUSPjBnjaTkpPl0Nazp8vb0L99FM5/JW+iD8KOhSU3dmtWzc2bNiAnV3O3doU\nY15BQUGhGJCaKkdqq1BBjsxmyOTaCp8VLFm9hIjSEUS0iqD6uepYPLJg7IixjB5e/DfFCSEI/S6U\ni4vDmWbrgXtLE1asyNVezBwf95tvJjNlStHNgxAQFsDsQ7M5cucI41uNZ0zzMZQulfcs3UJoiIjY\nyt27C0hNjcDJ6RMqVx5eZJJQvfToM+YzEEJO4rhgARw7Judv+fBD/ZFqFDIpKLvTxcWFu3fvUr58\neVT/ue/dv3/foLrKWstLjlqtLmwVShRKfxqPktyX6bHpaNP1bLTMBq1WTklgZgarVxu+Sj7KexTN\n32xOZGIkNicgNS2VGZ/PYJT3qNwpXsSQJImqn1Wl+cKqzA0/yd1LqfTq9SR0d37xzz+bCQj4gZ07\nt+TvgfKAeyV3fAf4sn/ofgIeBlBjSQ1mHZxFTHJMntqVJBMqVOhHkybHqFdvHTExfhw/7srt21NI\nSXmQqzZL8r1eGKiz+0KS5Hwn27bJRn14uBxGcuTIZ0NeKhQKISEhaDQaHj58yIMHDww25CEXxnxS\nUlJOqygoKCgo/IfQCC73u0zYmjDD5AV8/DHcuwcbN+oPwpIdS08uZWfgTsokmNHeTCLh4aPMTZwl\niQoDK9DiLze+CDmFfUIcHTumcv9+IunpcaSlRZOaGkF6+mO9ddPSIomLO8vjxyeJjT1GTMxhoqP9\nSEjQNW7Wrl1Bx4612LLlI8aMSeSvvybz2mv1Wbt2RX6fYq5pUKEBG7w2cHj4YW5G36TmkppM95tO\nVFJUntsuW7YNDRpspkmT42g0cZw6VZ9r14YTH29YBA6FQqROHfjpJzlUsJOTnDH9jTdArS6QBEkK\nuly6dIl27drRoEED5s+fz44dOwyum62bTXBwMAsWLMDe3p7PP/8cKysrdu7cyccff8ytW7eMpnxO\nUdxsFBQUijNB04OIPRJLo72NUJm+eD5FCDmHzKBB8l42Q1l9djVffv859hckGriZMWpkGKtWlefq\ndQ2jRozE23sUlpY1dOolJQURF3caIdIRQvPfezpWVrWxtdXNYhwXd5ZHj7bpyJcp05KKFQfryEdF\n7ef+/eWZchn17O27UbXqRB358PA/CAqaqtN+xYpDqFlz4TOyiYGJnP1mNqlD5pOuNaFUKVNMTEwB\nEypVGqojDxARsZmQkK+RJBMkyRRJkuXLl++Js/OEZ2SFEGza9BX//DOf995LZPVqUzp2fJP+/adi\nY9OoWAySbkXdYs6ROWy9tpWRTUbyaetPqWBdwShtp6VFcf/+T9y7txRr64Y4Oz7no1gAACAASURB\nVE/Azq5zseiXYo9aLb+y4ukpv15EUhL8+issXChHdZowQc5PkN8+a8WAgrI7O3bsyIoVKxg1ahTr\n16+nV69eBm+EzdaYb926NcOHDyc4OJjU1FTMzMzYunUrq1evpm3btkY9gQzOnDnD0qVLEUIwb948\nKlTQ/YFRjHkFBYXiSuTOSK6Puk6zM80wr5i/WVMvhV/CytSKi4cP8eefoxkxIpXVq81o0cKJNm3K\n4+DQBxeXyTr1oqL28+DBCuCJcStJJtjavkqlSsN05OPizhAZuUNH3tq6Ifb2nXTkk5JuEx9/LtNo\nzqhTqpQT1tZ1deTT0mJISwvXMbZNTKwxNdX1AU99lMql3pfYmVaRH0KqsGWLlG1m3NywY4cv69e/\ni4WFE4mJIXTr9hpubhdxdZ1BpUpDjXegfCYkJoS5R+ey8dJGhrsPZ2KbiVQurSexVC7QalN4+HAD\nd+8uACScnSdQocLgZ5JQRUeriYlRExIyAwAXly8BsLX1xM7O0yh6KOQCrRZ27pQ3y96+LWcXHzlS\nzor9klKQxvyBAwfo0KEDfn5+me+GkK0x37ZtW44cOQJAtWrVaNeuHStXrsTiBXGN84K/vz/169dn\n7969mJub07t3b12FFWPeqChx0Y2L0p/Go6T1ZVJwEmdbnqX+5vrYtrUtsONu376O9etHIUQlVKoo\nhg71oUePkhHRJjs0yRquDbuG3yVzZjysyU8rJIwV5XTZsjm4utbGysqexMQoQkIC+eCDzxEiHZWq\n+M1i3nt8j+/8v2Pd+XUMaTiEz175DOeyzkZpW05CtY/Q0AUkJFzC0fGj/5JQPclzoFZLBATAJ58o\nz3VjYbTfztOn5c2ye/fC8OGyYe9snGujOFFQdme/fv3o1KkTa9asYfz48fzxxx9s3brVoLrZrvGa\nPbW0Ym9vz9q1a/PVkAdo06YNV65cYf78+bi7u+frsRQUFBQKkntL7lF1UtUCNeQBQkPvMWzYb4wZ\n48PQoT6EhJT8JFwmFia4bXDj9Z4q5ltd4uMPtSxZYpy2P/xwMj16eCFJEj16eDFmzCQkSdJryGu1\n6Zw+3ZSbNycQG3sMIQzf9FxQOJZx5Ptu33PlwytYmFrQ+KfGjP57NMExwXluW05C1YXGjffQqNEu\nEhOvc+JETQIDPyYpSXbXFQL+/Rdlkq4o0qwZbNgAZ8/KM/aNG8OQIXJZweisWbOGoKAgypcvz+nT\np/n5558NryyywdPTU+/fOeX48eOZ9TUajRg9erRo3bq18PT0FDdv3hRCCDFt2jQxaNAgcfLkSZGa\nmioiIyPF2LFj9bb3HJUVFBQUiizadK3QarUvlDtyRIhbtwpAoZeEeyvuCd/yp0Vtl3QxcaIQGk3B\nHVur1Yq4uAvi9u3p4sQJN3H0qKO4cWOsiIk5UnBK5JCIhAgxZf8UYT/XXgz/a7gIjAw0avvJyffE\nrVuTxZEj5cXFi33F3LmIfv0QO3b4GvU4CvlAdLQQ8+YJ4eQkRIcOQuzYUbA3VCFRUHbnrFmznilP\nmjTJ4LrZutmYm5tTrpyckTAqKgr7/9J/5yTu5bx581i/fj02Njb4+/uzZcsWduzYwZo1azhx4gRz\n5szhr7/+ypT38/NjzZo1mJubM3r0aFroST+suNkoKCiUVM6dg65d5ag1HTsaXm/d+XWkpKcwsunI\n/FOuGBO5O5KTb9/kq/IeVHM355dfoFSpgtcjIeEKERGbSUsLp1atHwpegRwQnRTN4hOLWXpyKd1q\nduOLdl8YNTuwj88S1q6dg7NzGO+9B+vXVyE42JZ33hmLt3fxz4FQoklLgz/+kP3qU1LkJFRvvw35\n7L1RWOS33fnzzz+zevVqrly5gpubGwBarZbU1FTOnTtnWCPGHFVkZfPmzSIwMFC0atVKCCHE+PHj\nxaZNmzK/d3R0zHGb+azyS4efn19hq1CiUPrTeLxsfXn9uhCVKgmxeXPO6v16/ldRZUEVcTXiqoiO\nPiiuX39fr9zL1p9ZiQuIE36Ox8TrDRJE+/ZaERWVt/byoz9TUx8JjSbN6O3mhZikGPH1oa+FwzwH\nMeDPAeJC2AWjtKvVasX27X+It95CLFqEGDLETCxeXE2Eh/9l0AqWQvYU2L2u1Qrx779CdO8uRMWK\nQsycKURERMEcuwDJb7szOTlZBAUFiREjRojg4GARFBQkQkJCRHJyssFtZOsz7+Pjk/n35cuXM/+e\nMWOGwaONvn37YvpUUOS4uDjKPLUj2sTEBK226PkQKigoKBQkoaHQpQvMng19+xpe7/eLv/PZvs/Y\n984+HKRbXL7cDweHfvmnaDHGprENrY67M111GZeoGNq1E9y5U9haPcu9e0s5dqwy166NIDJyN1pt\namGrRFmLskxpN4Xb427TrHIzuqzvQp9NfTj7IG9+0xn5DlJTYetWSEuzwMGhHyEhX3HmTBMiIrYU\nyT0GCk8hSfIS4j//wIEDEBICtWrBmDEQWPL35hiLkJAQUlNTmThxIikpKaSmppKcnExISIjBbWTr\nZvN0SJzs/jaE4OBgBg8ezLFjx5gwYQKtWrWif//+ADg7OxMaGmpwWyD/AAwbNgxXV1cAbG1tcXd3\nz9y5nZFJTikrZaWslAur3L59e0JmhRDcOBjKPl8+OVnF+PGv8t570KyZ4cfbeGkjH/79IfMbzad7\nMwtu3hxPWtp0wK3Qz78olzWJGhx+cODX4PKsfXSdOXMkRowoOvpBGDVq3CciwpfHjy8DrWnVajUW\nFk5FQr/k9GSu21xnnv88qkZV5Z3G7zCm/5hctTdu3EiSk1czaBAkJvqyb98eevceTMOG8YSEzOTU\nqUgqVhzKm29OR5JUReL8lfILylFReJ47Bz/9hLpOHRg4EM+PPgJJKhr65aLcoUOHfHWz8fT0zDYX\ng8H2dnZT9p7ZbID1zOFm2KCgoEw3m82bNwtvb28hhBDHjh0T3bt3z1FbQihuNgoKCkWfOwvuiNPN\nTov0pHSD5A8dyln7iamJwuMnD3Eh7IK4e/dHcfSoo4iLu5gLTV9ONGkacW30NfG1S6BwKKcV+/YV\ntkb6SUoKFaGhi0Vqah59gvKBpLQksezkMlF1UVXReV1ncSg4hxfxf/j5Ifz8dJ/rWq1WPHr0jzh9\nuqU4ccJNhIX9LrRaw+4nhSJAfLwQy5YJUaOGEC1aCLFpkxBpRcuFzFCKg92pMtrQ4jlkjDj69OmD\nhYUFr7zyChMmTGDRokUFcXiF55Ax8lQwDkp/Go/i2pcxR2K4M/cObn+6YWJhYlCddu1ydgxLM0tO\njzpNfYd6PH58DA+PQ9jYNHhuneLan/mBylRF7eW1eXuMOV+pLvPWQC2//pqzNgqiPy0snHByGouZ\nmZ3OdxpNMhERm9FoEvNdD31YmFowpvkYAj8OZED9AXhv86bDLx04EHQgx7OYAQG6n0mSRLly3WnS\n5Bg1ay7i3r2lnDxZn7Cw9Wi16UY6i5JJkbjXra1ld5vr12HSJFiyRHbBWbIE4uMLW7sSh2l2X0RF\nRbF3716EEDp/5wRXV1f8/f0B+eZcvnx53jRWUFBQKKKkPkzlyqAr1PWpi6WrZb4eSyWpQFJRr966\nfD1OSUWSJKp+VpV+ruGUeT+AKRMbc/euCZMmya7ARZ20tHDu31/BtWvvYm/fBQeHftjb98DU1KZA\n9TA3MWdEkxF4u3vz+8Xf+eCfDyhvVZ5pr06ja42u2boPZGSAdXH5kgcPggkK+grQzQCbEavezq4z\nMTF+BAfPICRkBlWrfkHFikOKZaKulwoTE+jTR34dPy4noZo5E0aMgI8/BkfHwtawRJCtz7y3t/cz\nN2FSUhIAlpaWz2yOLWgyfOa9vb3x9PQsMj5VSlkpK+WXu6xN13KoxSFoAJ7rCl8fpWx42cPcA/Wb\nNxgnYmnU2pQtWzwxNS06+j2/HEudOo+IiPAlKuoQ8DaenisKTR+NVkO4QzizD89Gc1vDO43eYco7\nU5Cy+Eyrg9Ws/WstvwT8AtXgy/ZfEhwQjHsldz4Z9Mlzj+fuLhEcPBN//ytUqPA2fft+jUplXkT+\nH0r5heWqVeH771H7+ECbNnh+9x00alR09MtSzm+feWOQrTEfEBDA1KlTqVixIoMGDWLgwIFIksSi\nRYsYOnRoQeuZiRJnXkFBoSgitIKH6x9ScUhFJBP9s5FCwLffyhFr6tQxvO3b0bepblfdSJoq6CPx\nZiLHu15hutYN+waWbNgoYW39rEy0OpoYdQwhM+QoEy5fugBg62mLnaeuK0xBk5YWTXp6NJaWhX+t\naIWWLVe3MPvQbCRJYmq7qfSp10deUXoKaYZ8r4gvc/5cj4k5QkjITBITb+DiMplKlbxRqQohgYBC\n7oiKghUr4IcfoEEDmDBBDutVxJbGCsru/OWXX/j2229JTk7OPO7t27cNq5ydM32rVq3E3r17xcaN\nG4WVlZW4fv26iI6OFi1atMh/T/7n8ByVFXLByx572tgo/Wk8SmJfzpkjRP36Qjx6ZHidv6//LSp8\nV0Hci7klgoK+EhpNSq6OXRL709ikPkoVx1ufFb1co0XzZloRHq5fzg8/sYhFBatcHgkJmSvu3/9Z\npKbm4OIzAlqtVmy7tk00W9lM1F9WX2y4uEGka55sZOUrBMPy9lyPifEX58+/Lvz9ncXdu0tFenpS\nXtUu1hS7ez05WYi1a4Vo0ECIhg2F8PGRPysiFJTdWa9ePREYGCiSkpIyX4aiys7IL1WqFJ07d2bg\nwIE0btyY2rVrY2trS+nSpY0w/lBQUFB4uVi5Un7t3Qv/Jdd+ITsDd/LutnfZPuA3wm69TXJyMJDt\nz7ZCHjErZ0azA434usU9GoWF0bql4ObNwtbKOFha1iIqahfHj1fn/Pku3L+/ktTU8Hw/riRJ9KrT\ni5MjTjK/y3x+OPkD9X+sz7rz60jXpoMALpKnmc+yZVvTqNFO6tffTFTUHk6cqMHdu4vRaJKMdyIK\n+UepUjBsGFy4AN99B7//DtWqwZw58uz9S0KNGjWoWbMmFhYWmS9DyXYD7NP+8qWeynut0Whyqabx\n8Pb2VnzmjVTO+Kyo6FPcyxmfFRV9inO5JN3fERGezJgB8+ad4MaNJKpUeXH9PTf3MOTPIXzb8DNU\nDydQxq4jd+/2JCzsyEvfn/ldbr+hPVO+CCLxp420aFaJXXs60LLlk+8B3HEvMvoaUnZw6MPly3bA\nu1SunEBEhC83bkwBfsfTs0u+H1+SJCzuWjC72myEq2DmwZlMWj0JrgHmsGXHFsqVLpen4509mwB8\nStOmZQgJmcVff82kQoWBeHl9h4mJdZH6fyhlPeWDB6FUKTz37oXz51F/9hl88w2ew4fDJ5+g/i/L\nW0HrV1BYWlrSrVs33N3dM5OqffPNNwbVzdZnvkKFCnTq1AkhBAcOHKBjx44AHDhwgIcPHxpP+xyi\n+MwrKCgUFdJi0jCzfX40jZAQaNFCnpFv3Niwdv+9/S+DNw9mq9dyTCMmUbHiUFxcpmYbGUQhf7i/\n6j6/fhbDd9TFZ52K0qVBrYaMROhffim/e3rKr+KGVpuOSpXtnF6+ssJnBXOWzyHEIgQ6gsNxB+wf\n2zN+5HhGDx9tlGPEx58nJGQ2MTGHcXb+lCpVxhR4tB+FPHLvHixdCqtWQYcOsl99q1YFqkJB2Z1r\n167V+Y0fNmyYQXWzNebVarXeE5Akifbt2+dS1byjGPPGRf3ULLJC3lH603gU9b584POAsF/C8FB7\nvFA2PBwqVDC87ZtRN3kY/5ByiT9jY+OBk9PHedBUpqj3Z1Elak8UmweFMpUGzPzWhNGjQZIE8D5a\n7U8lcoAVHv4nd+8uxsGhHw4OXlhYOBv9GEIIfLf7MmD+AKgBlncsMXE1YczgMYxtORbHMsYLWRgf\nf4k7d74mOvoATk6f4Oj4IaamZYzWflGjRN7r8fGwZg0sWiSHs5wwAXr1kkNf5jMFZXempaVx6tQp\n0tLSEEJw//593nrrLYPqZjskL3EXgoKCgoKRiAuI4/Znt3E/6G6QfE4MeYCa9jWpaV8TIVojZYn+\noVCw2He1Z7DaHNtuF/jsiwaEhJgCu4EwNm/eQ79+3QpbRaNTvnxvTExsiIjwJSRkNpaWNXFw6EfF\nioMpVco4RnaGGwHpwEkwdTBlbue5XEu/RsPlDelVpxcTWk+gYcWGeT6WjU0D3Nw2kJBwlZCQrzlx\nogaOjmNxdPwYMzPbvJ+MQv5jYwNjx8qJqLZulcOCffYZjB8P3t5gZVXYGuaZPn36kJ6ezt27d9Fq\ntTRp0sRgY155SrzkKIM246L0p/Eoqn2ZFpPG5X6XqbmkJtZu1i+ukAeMacgX1f4sDtg0tqHXaTd6\nl5rPwnk9AD/gL8aN3UX9+m+wYsX6wlbRqKhU5pQr9zp16/5MmzYPqFZtFklJgcTHXzTqcQKDAqEm\nMAB8JvoQGxHL4tcXc3PsTeqUq0PX9V3ptr4b+2/vN8rMqLV1Pdzc1uPhcZSkpFucOFGToKAvSUuL\nzvvJFCFK9L1uagr9+8sJqHx8YN8+cHGBqVMhLKywtcsTjx49Yvfu3bRq1YrTp0+TmGh4dudiacx7\ne3tnbkxQq9XPbFJQykpZKSvlfCv7qTna8yj23eypOLhi4eujlAusXMqxFOZ9oikvBaFCA0hEPrxN\nYvhtzEVaoeuXX+VDh45ib9+ZOnVWcPGihV75lJR7uWq/dePWYAJI4NXTi1aNWqFWq7G3tGdyu8ms\ndV9L46TGjNs9Do8VHnzx8xfs/3d/ns/Pyqo29eqtJSFhMYcPn+bEiVrcvj2V/fu3FXp/K2UDy5KE\nOj0d9bhxcPQoREWhrlkTdffucPly3ttXq1F7e6MuQDc6a2trhBDEx8djZWXFo0ePDK6brc98UUXx\nmTcuanUJ9K0rRJT+NB5FsS9jj8dy69NbuPu5oyqlOxcSEwMDBsDatVClimFtHrlzhBN3T+Bdxw1b\nW09MTCyNq/R/FMX+LG4IIZj06SwWfH8fU8JJwZH3mljz3fefU7Z1WVSmxXJ+LE9otemcPFkblcoK\nB4d+VKjQHysrN4P3EkgzJAgCsTb757pWaNlzcw/zj83nRuQNxrUcx8gmIylrUdYo55CUFMSdO98S\nEeFL5cojcXaegLm5g1HaLgxe2nv90SNYvhyWLYMmTWDiRHnTbF4McklCIm+hUw1l6dKlREVFYWZm\nxrZt27C2tubff/81qO7L98ujoKCgkEvKtiqL+yH9hnxiIvTsCfXqQeXKhrXnH+pP3019cbe6zY0b\no0hNvW9kjRWMiSRJhIXH0IggqpKIg1SVrVcrcu7DYPwr+nNlyBUebnhIWnRaYataYKhUprRseZM6\ndVai0TzmwoXXOXXKjTt35hnvGJKK12u9zr9D/2XboG2cCztH9SXVmbh3IqGxoXlu39KyGnXqrKBZ\ns3NoNHGcPFmXW7f+R2pq4UXuU8gF5cvDtGkQHCyn2f7oI9mo/+03SCv69+RHH33EtGnTmDx5MqtW\nrWLHjh0G11Vm5hUUFBTySFoa9OkDdnbwyy+gMmCa5Pjd4/Ta0JPfX3ud0mnHaNRoH5aWrvmuq0Le\nWDZnGROmuJJCd/7+cycLFzkRndiY7WuTMTkRReSOSGLUMdh42FDujXKUe6McVnWtSmTUG30IIYiL\nO0VS0i0qVhz8Qnlphtwv4sucPdfvxN5h8fHF+AT40L1Wdya0noBH5RdHljKElJR73Lkzl4cP11Op\n0jCcnf9HqVIGLrUpFB20Wti1CxYsgMBAeQPtqFFQNgcrOgU4M3/p0iU++OADoqOjGTZsGPXq1eON\nN94wqK5izCsoKCjkAa0W3nkHHj+GLVvA7Plh5wE4cfcEvTa8wW+ebSnLbRo12kOpUpXyX1kFo5Bh\nlwshv6ZPB19f2L9fjpqnSdIQ4xdD5I5IIndEIplJmYa97au2eld2XhYePz4JCM5GJqIOOYgQgpMb\n/6XFoNeQJAlPV088XT0Nbi8mOYZVZ1ax+MRi6pavy8Q2E+lao6tRBk8pKQ8IDf2OsLC1VKw4BGfn\nz7GwcMpzuwqFwNmzslG/e7ecbXbcOHnj7IsoQGO+Y8eOrFixglGjRrF+/Xp69erFmTNnDKpbONki\n8oiSAdZ45e+//x53d/cio09xLyv9abxyxt9FRZ/syhculOXePQ927YKjR18srxVaJt+azLpOAzCN\nPUgCczINeaU/i3b5++/VBATAMFwJ4zje3vL/zdvbExsbaN5czYIFMHiwJ+W6l+Oi1UVEf0Hzcs2J\n/CcS3/G+JAcn07FrR8r1KMdF24uY25sXmfMrmPI+LC03U0qbiCctOXmqDDZR52iROB5r63IQDLiS\no/b/5/k/xrUax1c+X/Hhjx9iVcuKCa0nUCWyCuYmue/fY8euA71o0+ZzQkMXsGqVG7a2Hejf/wcs\nLKoWkf7ULWd8VlT0KRLlJk1QjxwJvXrheeqUXG7cGAYOxHP06OfW18eJEyeYNGkSfn5+3Lx5E29v\nb1QqFQ0aNGDZsmVIksSqVatYuXIlpqamTJ06lR49emTbXga1atUCwNHRkTJlcpALQRQziqHKRRo/\nP7/CVqFEofSn8SgKfRnxd4SIOhD1Qrn09Jy1m5qeKjSaZJGeHp9LzXJOUejPkoIffmIRi3Q+X7pU\nCGdnIa5ezb5uSniKeLDugbg04JI4bHtYnG5+WgTNCBKPzzwWWq02H7UuOmi1WrFixXTRrp2DeOcd\nc3HgAOK991xEx45uwsfnpzy3vffmXtHl1y6iyoIqYs7hOSIq8cX3sCGkpISLW7cmicOH7cW1ayNF\nYuJto7RrbJR73QBiYoSYP1++Ydu3F2L7diE0Gl050LE7586dKxo2bChat24thBCiZ8+e4uDBg0II\nId5//32xdetW8eDBA9GwYUORmpoqYmNjRcOGDUVKSspzVfLy8hLLly8XzZs3F7///rt48803DT4d\nxc1GQUFBQQ+JNxM51+YcDXc0pEyLkpstUiHnqCU1AJ7CU+e7tWthyhTZVbdx4+e3o03TEnskNtMd\nRxOvoVwP2R3H7jU7TKzzP7tlYSGEYMcOX7ZuncDQoaGsW+dE376L6NHD6xkXGa02DZXKAN81PZwP\nO8/C4wv5+/rfDG08lE9afYKrrWuedU9Li+Tu3e+5d2855cv3xsVlCpaWNfLcrkIhkJYGf/4pu+Ak\nJMCnn8p+k5ZyVDEhSah41s1my5YtNGrUiHfeeYdjx47h5OTE3bt3Adi+fTt79+6la9eu7Ny5k+XL\nlwPQt29fpkyZQrNmzbJVJTY2lm+++YaLFy9Sr149vvjiC+zt7Q06DVUuT19BQUGhxKJJ1HDZ6zKu\nX7kqhrxCjvD2hsWLoUsXOHHi+bIqMxV2HeyouaAmLa+3xN3PHat6VtxdfBf/yv5c6H6Bez/eIzkk\nuUB0L0gyMsAmJsawdq0bCQmxT7LC/kd6+mOOHnXgwoXXCQ1dSHz8pRxN5jWu1Jhf3vyFCx9coJRJ\nKZqubMog30Gcvn86T7qbmZWjWrVZtGwZSKlSzpw505KrV4eRmHgjT+0qFAJmZvDWW3D6NPz0E2zf\nDtWqwYwZEBHBHj1V+vbti6npEy/1p6/J0qVLExsby+PHjyn71EbbjM/1cefOHe7cuUNsbCxjxoxh\n+fLljB07lvj4eINPQzHmX3Ke5xOmkHOU/jQehdWXQghujLmBdQNrqnygG8EiKSln7YXFh5GcfJ/0\n9DgjaZg7lGvTuAQQkO13/fvDmjXwxhuQk263qm2F83hn3P91p3Voayq9W4nHJx9zpvkZTjU6xe0p\nt4n1j0Voiv/qdLQ6moA1J2ny10Qa/zKCbhZzCVhzkmj1k2yspqZlaNUqiMqVR5CYeINLl3px7Jgj\nt259nqNjOZVxYm7nuQSNC6KlY0u8/vDCc60nO27sQCu0uT4HMzM7qlX7ilatbmFpWYtz517hypUh\nJCRczXWbxkC513OBJIGnJ+zYAX5+rN+/nzcqVeKwAVVVqiem9OPHj7G1taVMmTLExT35zY+Li8PO\nzk5vfVdXVzw9PRk4cCCDBg3KfA0cONBg9RVjXkFBQeEpHqx+QNypOOqsrKMTEePQIdl1IiXFsLYu\nPrxIV5+GnDzTksjIv/NBW4WiSo8esGmTbNjv2pXz+qZlTanQrwL11tajzYM21F5ZG1RwY8wN/Cv5\nc3XoVcI3hZMWU/TjZ+vDztOOqVvm0SLlVSQkhv/yAV9snoud57MGj5mZHQ4OXtSp8xOtWt3Gw+Mw\n9vav5+qYZUqVYXzr8dz8+Cajm45mut906v9Yn9VnV5OcnvvVD1PTsri6TqVly1tYWzckIMCTy5cH\nER9/KddtKhQe6ocPCezYkbLdu3PQAHkPDw8OHpQld+3axauvvkqLFi04fPgwKSkpxMbGcvXqVRo0\naKC3vq+vL82aNaNChQqMGTOG/fv3c+zYMY4dO2awzorPvIKCgsJTRKujMa9kjnVd62c+P3cOunaF\n33+HTp1e3M7l8Mt4/9mebxsK6taYjaPjB/mksUJB8zyf+awcOwa9e8uJKb28jHP85NBkIv+R/exj\nD8VSumnpzNCXlrUti1VM+5z05Yu4d28Zjx79hZ1dF+ztu2Bt3RBJyn7OUgiBX7Af8/3nc/bBWT5q\n8REfNPuAclbl8qRHeno89+8vJzR0AWXLtsXVdRo2Ni/YQKFQ5Njt68ue/v35Ht3QlMHBwbz11lv4\n+/sTGBjIyJEjSU1Nxc3NjVWrViFJEqtXr2blypVotVq++OIL+vTp89zjxcTE4Ovry7Zt27C3t2fw\n4MF069bNIF2LpTE/bNgwJTSlUlbKSrnAyo6OnrRvD6NHX6J9+0cvlK9QvwKjfV9lau1kSpl8gqfn\n7CJ1Pko5b2U6yG/4YZB82bKedO8Ow4er6dLFuPpokjU0Tm9M5I5I9m3eh8pcRZcBXSj3RjnOac+h\nMlMVen89rxzQIQB33PEUxnie7wACqFLlAdHR+0hKegQ0pWnT7yhd2v25gEHYjgAAIABJREFU9S+F\nX+J/K//H4TuH8X7Tm/GtxhN6ITRP+vz77y4iI3fg5LSV0qVbEBraHSur2kWq/5Vy9uWJI0dSYfVq\nPqdg4sxncOzYMRYsWMCRI0cICwszqE6xNOaLmcpFGrVanXnhKuQdpT+NR1Hpy7t3oV07+OILGDHi\nxfLXH11n2J/t+NotGfcGGyhX7sWxhQuCotKfxZlodTQx6hgAjgcfp5VrKwBsPW113EOycuWKvCl2\n6lR4//380U8IQfz5eCJ3RBL1TxQJVxOw72xPuTfKYf+6PeYVzPPnwHlALakJIIBPxCdGbzspKYjo\n6H2ULdsWa2s3g+o8iHvADyd/YOWZlXi6ejKxzURaObXKkx4aTRIPHqzizp15lC7tgYvLNMqUaZGn\nNp+Hcq/nnYx7PWRGCB3okO925/nz59mwYQM7d+7Ew8ODt956i9dee+2ZjbbPo1gmjVJQUFAoKE6f\nhg8/NMyQB7Ays2JCu8W0dHVTltZLGHaedplGe4g6hGqe1Qyu6+YGBw/KLlrx8TBxovH1kySJ0u6l\nKe1eGteprqSGpxK1K4rIHZEEjgvEup51pjuOdSPrYuWOkxssLathaTkq2++vXRuBlVUt7Oy6YGPT\nGElSUbl0Zb557RumtJvCmnNrGLx5MI6lHZnYZiI9a/fERJXzcKEmJpY4OY2lcuVRhIWt4fLlflhb\n18fFZTply7bOyylm8rTxGUAALl/K2U0NGWgq6JJxr4fMCMn3Y7m5uSFJEoMHD2bdunVY/hcW8/bt\n29SuXdugNpSZeQUFhZea9Nh0TMsq8xoKBUNoqGzQDx4MX34pB9EoCLSpWmIPyzHtH/39CJEisO8h\nz9rbdbTDxKpwYtob02c+JwghiIz8h+jovURF7SU9PQo7u07Y2XWmUqVhmb726dp0tlzdwnz/+cQk\nx/Bp608Z1ngYlmaWuT62VptCWNgvhIR8g5VVLVxcvsTWtq1Rzquw+rOkopbU+T4zn7GKom9w7efn\nZ1AbijGvoKDw0pJyL4UzLc7gcdQDS9fcP5wVFHLCw4eyy02nTjB/fsEZ9BkIIUi6kZSZrCruTBxl\nXy0rz9r3KIeFs0WB6VJUjM/k5DtER+8jPv48tWot0fleCMHhO4eZ7z+fE/dO8EGzD/iw+Yc4WDvk\n+phabSoPH/5KSMjXWFi44uIyHVvb9nlaMSkq/VlSKAhj3hioClsBhcIlc0OXglFQ+tN45HdfatO0\nXB54Gccxjnky5IUQaDSJRtQsf1CuTeOSl/6sWBH8/ODIEfjgA9DmPtR5rpAkCas6VjhPcMbdz51W\nd1pR6Z1KPD76mNMepznlforbU28Te7xgYto/L2Z/QWFhUZXKld/Ta8gDJCZexYUjrO8xHfWwA9yP\nu0/tpbV5f8f73IjMXbIolcqcypXfo0WL61SqNIwbN0YSENCeqKj9eTIei0J/KhQsijGvoKDwUnL7\n89uY2ppSdXLVzM+EgJs3c9BG1C02HnqFmzfH5YOGCiUZe3vYvx+uXYNhwyA9vfB0MbM1o8LACtT7\ntR6vPHyF2stqgwZujLqBf2V/rnpfJdw3nPTHhahkISNJZqSmhnPt2jAir7dnXPUYjg2YhpO1BW3X\ntKXPpj4cvXM0V0a4SmVGpUrDaN78KpUrjyIw8CPOnWtLVNQeg9uLjlYTFPQVDFsLXXcTFPQVQUFf\nER2tzrE+CkWDe/fuGSyruNkoKCi8dIT/Gc7tz2/T9HRTzOzNMj///HN5w+v+/S92fQiKDuR3Pw88\nyleic+vTmJnZ5rPWCiWRxEQ5/rylJWzYAKVKFbZGz5Ic8lRM+yOxlG7+JKa9VS2rPLdfHN1CkpPv\nEh29n+jovZQt2xa7Ct6sDVjLwmMLcbB2YGLribxZ981cbZYFEEJDePifhITMwsTEBlfX6djbdzfI\n/aY49mdRpjDcbA4cOMCyZcs4cuQIDx8+NKhOsZyZ9/b2zlziVKvVzyx3KmWlrJSV8nPL/6oJ+TqE\n+n/W5+iFo5nfz5sHmzYlMHbskUxDPrv2gqNv8Nchd2pYlcdKuzTTkC8S56eUi1X55Ek1f/0lDx7b\ntVOze3fR0u940HEcxzjSaGcjUjelEtIxhMSriQS0D+BH5x/5bcBvRPtFo03T5qr9p11CisL5GlK2\nsHCicmVvwsNHERjohpWZFWOaj2FFgxW8bvo6C44twOsXJ77yGciufTty3L4kmVCx4iASEn7gzp3u\n3L49mTNnmrFt2+xnNkSWlP4syuWCclmKj49n2bJlNGjQgAEDBuDl5UVIiOGRdJSZ+ZcctVqJR2tM\nlP40HvnZl9o0LSqzJ3MZq1fD11/LPsyOjs+vGxJ9g51HG1OpTD16tz2OSmWeLzoaG+XaNC7G7s/0\ndHj3XQgJgb//hjJljNZ0viCEIP5cfOYm2qTAJOy62D2JaV/esPsiP+PMFyaHL3zIw4frsZDiSDSp\nQyPX93Cp5IWlpeHhTDMQQsujR9sICZmJEAJX1+mUL/+m3uy2JbU/C4uCmJn/6KOPOHDgAH369MHb\n25uxY8eya9euHLVRLGfmFRQUFPLC04b85s0wfTrs3ftiQx7gw13jsbJ9kzfbnSo2hrxC0cfUFNau\nlePRd+oEUVGFrdHzkSSJ0k1K4zrdlaYnm9L8anPsu9rzaOsjTtQ4wdlXzhIyJ4T4i/Ev5QRcu0bL\n6Nc5lpoNDhGUWoWNZ6agPlafyw+O5LgtSVLh4NCHpk3PUq3aLO7cmcPp040JD/8DITT5oL1CQXLk\nyBGaNWtGq1atqF69eq7aUGbmFRQUXmr++gtcXMDDwzD5xLRErMzy7iusoKAPIeCzz2DPHti3T458\nU9zQpmiJORgj+9r/HYnQiMywl7YdbDGxfOJL7if5sZGN/KT9qUQnsYpIiGD5qR9ZdvpHWji2YGLr\nibzq8iqSJKHVphEXd4rSpVugUr0454UQgqioXQQHz0SjeYyLyzQqVBiAJJnI/Wmzip8e/1ai+zPf\nUatBrSZ4RjDV+CXf7c6jR4+yevVqjhw5glarZceOHdSrV8/g+ooxr6CgoKCgUIQQAmbNgt9+kzdj\nOzsXtka5RwhB4rXETHec+HPx2HraZhr3i50Wc4pTePt608OrR2Grm+8kpSXx64VfWXBsAWVKlWFi\n64n0qN6Mq5e9SEkJwda2A3Z2nbG374KlZY3ntiWEIDp6H8HBM3icGMrV9KZsnxiFeRV/bNv2oVoL\nNzxdPfF09SyYkyuBFPQG2MePH/Pbb7+xevVqJEni9OnTBtVTjPmXHMWP1rgo/Wk8jNWXaZFpPFj9\nAOfPnHM1UyWEKBEzXMq1aVwKoj8XLIClS2WDvsbz7bpiQ1pUGlF7olizaA1/n/mb6trqNKUpFypc\nIMg8iIEDBjL03aGYlTfDtJwpKtOS6Q2sFVp23NjBfP/53Im9wyetPmFo/Z6kJR4nKmov0dH7sLfv\nSt26Pi9sSwjBTz9NYP36FVRzTqFpSw1XrtTk9m1z3nlnLN7eowvgjEomhZk06ty5c3gYuGSs5DBX\nUFAosQit4Oo7V7Fys8qVQX4/Yh8R9+fTqNEuvZvNFBTykwkTwMYG2reX93S4uRW2RnnHzN6MioMr\nMmnQJOpvqs+mwZuQkEhPSGeg60BanmzJ5V2XSYtIIy06DdOyppiVN8PMwQyz8maYO5hn/v30u7mD\nOWblzTCxzl04yIJGJanoVacXver04sTdEyw4toDZh2YzsslIPm45h7p115KeHqu3blpaJCYmZVCp\n5LC6kiTx/vsLcHJqzYYfR3LoUCyWlrcYMsSLwYN7F+RpKeSC1q1b6/1ckiT8/f0NakMx5l9ylJk6\n46L0p/EwRl+GfB2CJk7D/9m77+go6q6B49/d9JCQhB4IJID03pSisAFBmiIgWEAI+iqCKCiiID6C\noKLioygPothAUBQ10gVpSw+GFjqGEkgIJb33nfePlUhMgACTnZ3s/ZzDgbv729279wzJ3dk7M/Vm\nWQ8qunIFTp6E++67+WPPxC7j6LEncKn+Jq3KQSMv26a6bFXP0aOhQgXo0QPWrIG2bW3ysmXOYDBg\ndDaSSy472YnRaKTOq3VoO/ifN6gUKOQl5Vkb+/iif2efzyZtf1rR++LywMA/jf61HwD+1fwX/ruS\nCwajtt+83RNwD8uGLONM0hnmhM2h+WfNGdB4ABM7TaR5teLXr4iJ+ZSYmDn4+pquGclpgMFg4GJK\nFt7OBi5dcqWgIIXw8CZUqfIwAQET8PJqpcG7EzezdOnSO34OGbMRQpRLiRsSORFygnbh7XCr6UZK\nCphMMGgQ/Oc/N37sqehvOH7yWSKdnuTl4Jt/zS1EWfvtN3juOevfnTtrnY065s2aR8brGXSgA5m/\nZHIu8hxjJ4+97edTFAVLpoXcuNwiDf61HwRy43KL3Jafmo+L378a/Jt8C+DkXrZ7/xOzEvl87+fM\n/XMurWu05pVOr9C9bvci3y7m5saRlLSJpCTrSM66dRmsXg11vY08Py2BL7/058SJAsaNe40ePXK4\ncGEenp6NCAiYQOXK/eSbxlLScszmVkgz7+BkjlZdUk/13Ekts6Oz2ddhH01/bIqfyY+sLHjgAWjV\nCj799MZXd4089wnH/nqF404jeS34y3IxLw+ybapNi3quWwcjRsCPP0L37jZ96TKj9XnRLfkW8hPy\nS2z0r/5d5MNBfB5GV+N19/SX9C2As6/zbf0cycnP4fvD3/Phrg9xc3bjlU6vMLTZUFycXIqsUxSF\njIzjbNq0j2VzX+GZN67w3Xe1GTToI/r1G4yi5AEQF/cz0dEfU1CQSkDAeKpXH4mzs5cqdSyvthi2\n0J3udt936nLMJiQkhJCQEEwmU+HVuq7+UJX41uKDBw/aVT56j6We9hF3btaZBp82IIII8jca+OST\nbtSuDQMHmtm69fqPX7FhBdvi3sK32lO8Efw5W7dutYv3I7HEAO7uZl5/HR57zMQ334CXl33ldzvx\nv69YqlU+rtVdCTeHQ1UwDbn+ekVR6NKuC3lxeWxev5n8lHzu9r+bvLg8tu/bTkFKAW2c25AXn0fY\n+TDyk/NpldsK58rOHPY4jLOPM50adsKlqgv70vfh7OtM105dca3qSlhUGE4+Ttz/0P0YXY3s3rGb\netTjyNgjrDu1jqlfT+WltJd4bdhrPNPuGfbv3l+Yn5dXU06e/Jks1xRmTPKiar1kjh07hpeXLy4u\nT+Dp2YSUlHooykhat27BhQtzCQ19nUqV+jB48Gzc3WvbxfZgb/GP/IgeyJ55IUS59vTTcOmS9Xzy\nLi43Xnsl4wo/HP6B8feMLzd75EX58+ef8OCDMHcuDB2qdTZ3xmwwA2BSTJrmUZYsuRbrnv3r7Om/\ndhwoNy6X/IR8jJ7GEsd9rrhdYX3ienZl7qJbm2483vVx6tSrg5O3E5999h4RE4+RmJNFyzcaUsW/\nImPHTqagIIPk5O0kJ28iKWkTWVmnqVy5P3Xrvs2FC59y6dIi/Px6ERAwAR+fjoB1bz8W63ELSr7y\nz9/X/JsCit5Xjtb8nvw7G9I3UC+/Ht/zvd33ndLMCyHKtZ07rReE8pTrPIly5NAh6N0b3nkHRo3S\nOpvb5wjN/K1SFIX85PwbzvunxqYSez6W3LhcKmVVwpxnZothC/Xy6/E0T/Ot+7ec5jQPVH2APn59\nijau7ilYqp3DcKL537dbsOTlYsnLBxTIc4UCIxjA4GzA4GSw/u1sAKd/3fbvv8vJGoywbt06fnnu\nF37gB7vvO3U5ZiPUYzabC79SEndO6qketWrZpcud51IeyLapLq3r2bIlbNkCPXtCRgaMG6dZKnfs\nIAcxYdI6DbthMBisB+X6uUDDG69Nzk5mwb4FLNm+hKATQWQsziDCEkGeex4TXp3AAz0fwOhiLF1D\na7RwJnoSF+O+wIAzVaoOIDDwTSpUaGSbN25nXCq7kEuu1mmUilHrBIQQ4k4VZBZgybfc2mMKsoiM\nfIG8vIQyykqIstWoEWzbBnPmwHvvaZ2N0IKvuy+vdnmV16q+RuS2SHIMOcyvOJ+UtBReWPACM7bM\nwNDEgFdzLyo0roBnA0886nrgXscdt5puuFZ3xaWyC84+zjh7u9KgyRzat99PzVpjSE7eRnh4E3bs\nqExMzDyt36rNnYs8Rwc6aJ1GqciYjRBC1xRF4fgTx/Fq60WdSXVK9Zj8/FQORvQlKj2Dh+8Nw9nJ\nrYyzFKLsxMbC/ffDwIHw9ts3PluTvZExG3UoisKYEWNYvm05l0ddpspvVQiqGoThUQNHrhyhfqX6\n3F3zbjrU6kCHmh1oUb0Frk6uN33e7OxznD07nYSEFfj43EtAwEv4+poKjykqL1fIvh69nJpSxmyE\nELp2Yd4FMk9k0uibRqxdC4oC/fpdf31ubhwHI3qxOfYi550HMth4819oQtizmjVh61br6VfT0+Hj\nj8Eo37s7lOTkrTw0PJGCgAQqpPqQ9XAqAzrXpdPdY6lQsTOHLh8i/EI4f174k//9+T/OJp+lRbUW\ndKjZobDBb1SlEcZ/nX/e3T2QJk2+paAgk8uXFxMZORaj0Z2AgAlUq/YYhw8PQFHy8fPrgZ9fD7y9\n22Ew6OMqvOWJ7Jl3cFrPfZY3Uk/1lKaWKWEpHHnoCG13t2XfRQ8GDoRVq6Bjx5LXZ2dHc+BgD9Zf\nzOKSS18+6ze/2C+v8kq2TXXZYz2Tk6FvX2jSBBYsACcd9FRan2e+PJn1ySzyJuXhnedNnZV1iDwb\nyeQXJ5e4Nj03nf0X91sb/Ng/Cb8QTkJWAu382xU2+HfXupvaFWsX2fOuKBYSE9cTEzOHjIxD1Kgx\nCk/PJqSl7SUpaRO5uRfw8elG48bf4uLiZ6u3rjpzlBlzlJmot6JYZF5k932n7JkXQuhSblwux4Ye\no9GXjfgr3YPBg+GHH67fyANEx37Figv5JLr24XMHauSFY/D1hT/+gAEDYNgwWLz45qdjFeXHlPFT\nME+wfjga/ODgG671cvWia2BXugZ2LbwtPjOevbF7Cb8QzqKIRYxbOw4Fxdrc17Q29x1qdaBK5T5U\nrtyHjIyjxMTM4dSp+VSpMpimTX/ExaUKyclmnJ19yvrtlilTkAlTkAlzsJlFLNI6nZuSPfNCCF06\nO+0slhwLlqfr060bfPIJDBly48eMXTOW3IJcFjy4QBp5UW5lZ8Mjj1hHbZYtA3d3rTO6PpmZV5ea\n9VQUhZjUGMJjreM54bHh7Ivdh5+HX5EGv0WVOqTGLyE29jMqVGhBQMAEKlXqjeFfP2MzM09x+HAf\nfH2tIzm+vsG4ula54zzLkl5m5qWZF0LoklKgkJ+v0LqtkQkT4Jlnbv6YlOwUvN28pZEX5V5uLgwf\nDomJsGIFVKigdUYlk2ZeXWVdT4tiITIhsrC5D48N59DlQwT5BtGxVju6VzVQ2xCGqxFqB7xEjRpP\n4uRk3fgUxUJGxmGSkqwXr0pJ2Y6HR31q1HiKgIAXyiTfOyXNfBmRZl5d9jj3qWdST/WUtpYXLkCt\nWmWfj97JtqkuPdSzoAD+7/8gMhLWrAEfO5x8kJl5dWlRz7yCPA5fOUz4hfC/G/w/ccs7yYi6njSo\nkE2We3fq1ZlIM/9uOBn/OZDDYskjLe1PCgqyqFTpfpvleyv00szLzLwQQteu18jHxS3H27s97u4B\ntk1ICDvh5ARffw3jx0P37rB+PVSx76kGoUMuTi609W9LW/+2jGY0ABm5GRy4dICImLUoyb/iktGT\n0F1G/sprSa0q3QsPsA306XzdU1ueO/cOKSk7/x7J6YGXV8tiozvCSvbMCyHKndjYBZyNeosWLdZS\n0buV1ukIoSlFgddfh5UrYeNG8PfXOqN/yJiNuuy1nnl5yZyJnkvshf+RVuDOtqTqLD0TTa4lv3D+\n/uopMqt7Vf/7MQkkJ5v/HsvZSH5+Er6+wQQGvomXV3Ob5C175oUQQkUxn8ZQ7dFquFa/8Xnhz59/\nnwsX5jPv/F10dd3Nc+2lmReOzWCAWbPA2xu6drU29IGBWmclHImLiy+N6v2HBkFTiI//jYCYj3nE\n3x2vyiM5ldeA8IvH+GTPJ+yN3UtFt4pFDrBtF/geDRtWJDs7mqSkTTg7V9T67dgdXTbzISEhhISE\nYDKZMJvNAIWzixLfWjxnzhxat25tN/noPZZ6qhdf/TdAkwtNiJl7gRf+MHBvcDwTJ7Yqtl5RFLZu\nHY5F2cGCyw2wOFWiQVqDIrPN9vT+bB1fW097yEfvsR7r2bmzmdhY6NrVxIYNEBurfX4HOWi9Dfl9\n7jj1HEK1akNYs+YzTpz4hbvu+pSQOiPomj8K14DXCWgZQHhsOKG/h7I4fjFRvlHU9qlN7cTaNKnS\nhCcebEKrGtUI2xFW+PzWn/9tgbo0bz4SH59u7Nx5ULV62jsZs3Fw5msaHXHnpJ7quVrL9CPpRARH\nEPpQB7YfdmXTJusexn+Lj19NVNRbfHCqMhajF0sHL8XFSU6yfZVsm+rScz2//hrefBPWrYMWLbTN\nRQ6AVZce65mdHc2FC//j4sWv8fW9j4CAl/Dxua9wlj7fks/RK0eLnEHnZPxJmlZtWjie096/PXU8\nMklJNpOcvInU1DA8PZtSqdIDBAW9dd25/JvRy5iNNPNCCLuVn5rPvg77WNumCT8fqsi2bdc/gC8n\nP4cnfnkExeDCT4/8JI28EDfw448wYQKsXg3t22uXh73OeOuVnutZUJDBpUuLiImZg5OTNwEBL1Gt\n2lCMRtdia7Pysjh46WCRBj82LZY2Ndr8PaLTihY+RnydUqlVa+xt56SXZl6XYzZCiPJNURTenfIu\nA04NYFONOiwKq8iOHTc+E0d2fjYNqzTnreC3pJEX4iYeeww8PaFvX/j1V7jvPq0zEo7OyakCtWqN\npWbN50hIWEtMzBzOnHmNWrXG4u8/usgFpjxcPOhUuxOdancqvC05O7nwCrbLji1nUmw4mXmZdKi5\nssgBtv7e/iQlbSYm5hP8/KwXsPL0bFri3nsFBS8vm7z9OyJ75h2cnr8qtkdST3Ws+nkVk554lwnD\nJvDxrqGsWm2gYUOts9I32TbVVV7quXEjPPEELFkCvXrZ/vX1OBZij8xRZsxRZqLeiuJS0iU6zukI\ngCnIhCnIpG1ydyA9/RAxMXOIj/+NqlWHEhAwgQoVmpT68RfTLlr33BeeAz8cTxdP7q3ViuDqXtzl\nmYp7/lFQcvH17Y6////h5xdc+Pj33aeztftbrF1r332n7JkXQtiNhV8sZPGniyHRi+j89vyy/gdq\n+n3Nri1P0LBhSOG63NzLFBRk4uFRV7tkhSgH7r8ffvsNBg6EBQvg4Ye1zkjcjqtNuzn47w9HpvLx\n4cjLqyWNG39Dbu4sLlyYz8GDwXh5taZ27Zfw8+t101l4f29/Hmr0EA81egiwfut7JulMYYO/+MwF\nDl5KpGWlKvQOOE2t5F9oUtudvSt/5OefluLfK51JL9nind4ZaeYdXHnYs2RPpJ53JgcnIlPzSYmv\nQiafEh7/f/g4nyKHf64amJ19noiI+6lVaxwBAS9qmK2+yLaprvJUzy5d4PffoV8/yMy07qm3pda0\ntu0LlnPlsZ6urtWpW3c6depM5sqVpZw+PQlFeZmAgAlUrz4cJyePUj2PwWCgfqX61K9Un8eaPwZY\nD7A9Hne8sMGfd2QchzYfwj0W3Lzyuc1jZ23KqHUCQghx1bPPDueJofeTV2AEDOQVGBj2aC+efXY4\nABkZJzhw4D5q1HyOlRedybfka5uwEOVEu3awaRO8+ip8+aXW2QhRMicnd/z9R9G+fQQNGswlIWEl\nYWGBnDnzBjk5F2/rOZ2NzrSo3oKn2jzF/P7z2ffsPlJ/TGXIgCG4OMGkSSq/iTIgzbyDu/ZcyeLO\nST1v35WfrhAVnk38xXhwdyMwcDS4uxF/MQ6DwUBa2n4iIoIJDJzO1PD9/HbiN/IK8rROWzdk21RX\neaxns2ZgNsM778CcObZ7Xb2cy1svHKGeBoMBP7/utGixijZtdpCfn0R4eFOOHx9BWtqBO35+DxcP\n3HIM7Drtyt69KiRcxmTMRgihudivYlkyOZWPDFUYPbY5ixfXoVIlVxITc4mMjCYn5wKHDvXmrgaf\nM2nnci6mX2TV46vwcCndV6tCiNK56y7Yts06S5+eDlOnoosxA+G4PD0b0rDhPOrWncnFi19x5MhD\nuLvXIyDgJapUeRCDwenmT1KCOg2a8/KCRkxjmsoZq0/OZiOE0NSFzy6wcFo6nygNWbvOQPv2f5+a\n8t0pvP76rMIDnDIyTzFuwzucSz7H6idW4+niqXHmQpRfly5Bz57WU1e+917ZNfR6Pi+6PZJ6gsWS\nR1zcr8TEfExeXjwBAS9So8ZTODuXcLXBm9DLeeZlzEYIoZnoOdF8NS2TuYaGbNhkKLx4zZo1v3L4\n8GesXRtauHbegVDOJp1l1eOrpJEXoozVqGEdudm8GcaNA4tF64yEKB2j0YXq1R+jbdswmjRZTErK\nTsLCgjh1aiJZWVFap1cmpJl3cOVx7lNLUs/SS92TypezcvjC6S42mw20agULF35Bjx7NWLHidUaP\nTmP58in06NGMhQu/YEz7Max+YjUVXCtonbouybapLkeoZ+XK1oNiDx2Cp56CfBWPN08yJ3F2+lkC\npwVyceRFzk4/y9npZ0kyJ6n3Ig7KEWbmS8NgMODj05lmzZbRvv1+wMC+fe04enQIKSk77X5v+62Q\nmXkhhCYq3lORPj95MrzmPxeEGjnyWSpXrkRo6EsYDFBQkM1LL71Lv36Db3o+YSGE+ipWhHXrrOeh\nf/xx+P57cHW98+f1M/nhZ/ID4Jz5HHVNcs0IUXbc3QO5664PCQqaxqVLCzl+fCQuLpUICHiJqlUf\nwWjU91XDZWZeCGE3FEXh228HsXbtSmLiFWpVdueppxbTr99grVMTwqHl5MCjj0JeHvzyC3jIsed2\nSWbmS0dRCkhIWE109MdkZ5+mZs3nqVnzWVxcKhVZJzPzQghxCxSN4bZwAAAgAElEQVTFwvJVwezc\nvYpjyVXY003hQLwPE6aM4Ytvv9A6PSEcmpsb/Pwz+PpaLy6VlqZ1RkLcPoPBiSpVBtCmjZnmzVeS\nmXmcPXvq89dfY8nMPFm4TsG+m/irpJl3cI4w92lLUs+SKYpC7uXc695vseRz4kQIQbUt9Oq7gPOW\nDDBCvpcLs97+jGdDnrVhtuWTbJvqcsR6urjAd99B/frQqxckqTTe7oi1LEsyM39rvL3b0KTJIjp0\nOIaLS2UOHLiPQ4f6kZi4kXC3rVqnVyrSzAshypRiUYh84RRj70vml19KuF+xcOzYUPLy4mjQ5Df+\nu/tzsnOyqbOtDsnpyRgMBpmXF8JOODnBggXQsSN07w5xcVpnJIQ63Nz8qVt3Jh07nmPLlko8+GA/\nDg14R+u0SkVm5oUQZUaxKJx87i9mrPLjRPWqbNhkoHLl4uvi41fg5NmZh5c9QtLOJKb2n8rQh4YS\nujqUyLORTH5xsu2TF0Jcl6LAtGnW0ZuNG6FWLa0zEiAz82pRFIXVq3/mx/mj+eH3ZLvvO+3ubDaX\nL1+mf//+hIeHa52KEOIOKAUKx58+yZvrqxAdUJnNGwz4+l5nrUdnui/uRZfaXfh0wacYDdYvDQc/\nKAe+CmGPDAaYMQO8vKBrV2tDX1dOSCPKCes3wkZyuf54qD2xuzGb2bNnExQUpHUaDkNmFdUl9bRS\nFIWjI04wZX1VLterzMbN12/kASq4VmBch3HM7TO3sJGXWqpL6qkuqafVq6/CxInQrRucOHF7zyG1\nVJfMzKvj3LlIOmx+Tes0SsWumvn58+czfPhw3N3dtU5FCHEHDAYDWZ2r4trZj3V/GPC+5iraJX1d\n6eniydNtn5bZeCF0aOxYmDnTOkMfEaF1NkKo4/nnp3B3Tlet0yiVMp+Z37NnD5MnT2bLli1YLBbG\njh3LoUOHcHNz46uvvqJ+/fq8+eabREZGcuXKFRo2bMjmzZt59913GTy4+FfsMjMvhH5lZZ3m2LEn\naNlyPS4uN9hVL4TQnZ9/hnHjYMUK6wGywvZkZl5dejnPfJnOzH/wwQcsWbIELy8vAJYvX05ubi67\ndu1iz549TJw4keXLlzNjxowijxsxYkSJjbwQQr/S049w6FBvgoL+g7Ozj9bpCCFUNmQIeHrCgw9a\nG3uTSeuMhHAMZTpmc9dddxEaGlr4iWbHjh307t0bgHvuuYe9e/eW+LjvvvuuLNMS15BZRXVJPUuW\nmhpORMT91K8/m01xnjy18qmbPkZqqS6pp7qkniXr1w+WLbM29mvXlu4xUkt1ycy84ynTPfODBg0i\nKiqqME5LS6NixYqFsZOTExaLBaPx1j5ThISEFB4k6+vrS+vWrTH9vQvg6g8FiUsXHzx40K7y0Xvs\nkPXMggpfVefMA/Wp3nBXCesjcHF5h0aNvmLauvX8EvML5qfN9pO/xBJLrGocHGxi5Uro29fMhAkw\nbVrx9WYzLFxoZtEiAOtpLqOizLRuDRMm2Nf70VN8bSNvD/noPdbLB6Myn5mPiori8ccfZ/fu3Uyc\nOJGOHTsyZMgQAGrXrk10dPQtPZ/MzAthP/LT8tn1wFEmnGpAuwEeLFhg4N/HsMbGfoW7exCz929i\n+cnlrB++njo+dbRJWAhhMwcOQN++8P77MGJEyWuu/ryQX+vqkJl5dSSZk0g2J3Mu6i2CFy2y+77T\naMsX69KlC2v//t4tLCyMli1b2vLlhRAqyk/JZ3v3ozwf2ZBOQzz44ovijTxAtRohvLbjJzad3cT2\nUdulkRfCQbRpA5s3w9SpMH++1tkIUXp+Jj/qTq8LIYu0TqVUbNLMXz3d3MCBA3F3d6dLly5MnDiR\njz/+2BYvL27g6tdKQh2OUs+8pDzMpqM8H9WIXiPc+d//DBiv89PEolio4VWDTSM2UcWzSqlfw1Fq\naStST3VJPUunSRPYuhVmz7b+KZnZhhmVT0nmJM5OP0vgtEAujrzI2elnOTv9LEnmJK1TEzZQ5leA\nDQoKYtcu6xytwWBgvgofz0NCQggJCfl77s4M2NeMlZ5ih5zxLsPYUerZ5FIT3kxoQLsHLtO//1kM\nhhuvn9l9pl3lL7HEEts23rYNOnc2c+QILFxowmAo/oHInvLVW+xn8iMC60n+/fGnrqkuZrOZc5zD\nhPb56TU+qI+R+bKfmVebzMwLYR+uXIFq1f6JFUUhOno2lSr1xstLRuiEEEVduQK9elkvLvXf/1rn\n5WVmXtgzs9lAcHDJFzu0J0atExBC6NO/G/kzZ17l8uUluLpW1y4pIYTdqlYNtmyBXbvgueegoEDr\njIQoH6SZd3D//ppT3BlHrKeiFPDXX8+RnLwdt4D5PLV6IhbFcsfP64i1LEtST3VJPW+Pnx9s2AB/\n/QUjRwIowGi73/OpJ7Jt3jlzlJnp5uksjNI6k9KRZl4IcUPZ57OJ3pFe4n0WSx7Hjw8nKyuS7Coz\n6fn9IPo16IfRID9ahBAl8/a2XlAqMRFgPZBAaOgfGmclxD9MQSamm6YTEqR1JqWjy9+4ISEhhZ88\nzWZzkU+hEt9afPU2e8lH7/HV2+wlnzuOl5r5oe1hOvT34MSJ4vdv2/YpV66cJ9bzRR5e9gQT603E\nP8Ffldc3mUzav/9yFEs9pZ72FH/33RKOHu0ELAF+ZvLkbQQFdeLll6faRX56jq89gNMe8tFzLAfA\nlhE5AFYI28g8lcmy+07xakYzPl3gxGOPlbzu+4glvLxhIssfXU6n2p1sm6QQQpcUReGXX9YxdOg2\nYBbu7lNYuLAbQ4c+UHg6ayG0JgfACl249lOouHPlpZ4ZJzL4ofNpJmU044tF12/kFUVhV8xuNo/Y\nrHojX15qaS+knuqSet4Zg8Hwd9OeDQwhLy+LefMMKIo08ndKtk3HI828EKKI/NR8ltx3hsnZTVm4\n1ImBA6+/1mAwMK/fPJpVa2a7BIUQ5UJkZDTQGxjL4sV9iImJ5vnn5TSVQtwqXY7ZjBw5Ui4aJbHE\nZRhXyu/IFdxxdv7n/uzs84SFrQUaa56fxBJLXD7i4GAAE4oCa9aYmTgRHn7YxHvv2Ud+EjtmnJRk\nZt26hQA88cQiux+z0WUzr7OUhdC9zMy/iIjoSZ06k6lVa4zW6Qghyol/XzQqIQG6dYNhw2DKFO3y\nEuIqPfSdRq0TENq6+klUqKM81jM9/RAHD5qoEfAasw+fJCM3wyavWx5rqSWpp7qknmoyF/6rcmXr\neei//hrmzdMuIz2TbdPxSDMvhIOz5Fz/Ak8pKWFERPSkWu2ZDPn9G7LysnB3drdhdkIIR+Pvb23o\n33sPvvtO62yEsH8yZiOEA0vemswnj8Xj80o9Jkws+tk+Ly+R8PDm+AW8y8MrZzGk6RBmBs+U08YJ\nIVTz7zGbax0/Dt27w2efccMD8YUoS3roO521TkAIoY2kTUl88HA8i9zqs7FP8S/pXFwq4R20jAd+\neoKJnSYyvuN4DbIUQjiqJk1gzRro3Ru8vKBnT60zEsI+6bKZDwkJkbPZqBTPmTOH1q1b200+eo/1\nUs8W2S14d0gyS5xr8+HH4TRtek+J62es/YQnaz5Z2MjbMt9r5z61rld5iKWeUk97ja2u//s8NNTE\nwIHw5ptmWrTQPl97j6/eZi/56D3WAxmzcXBms7lwwxV3Tg/1jF8Vz4zH01hRMRDzTiN162qdUcn0\nUEs9kXqqS+qpDuuYjRlFMd1w3fr18OST1r/btLFFZvol26a69NB3SjMvhIM59dUVnv6iEot/daZO\nnX9uz86Oxt29tnaJCSEczo1m5v/t119h3DjYsgUaNy7bvIS4Sg99pzTzQjg4RVGIinqTxMR1tG37\npxzgKoSwmVtp5gEWLoQ334Tt2yEwsMzSEqKQHvpOo9YJCG3paSZMD/RWT0WxcOrUBBISVvNn/iCS\ns5O1TqmQ3mpp76Se6pJ6qslc6pUhIfDKK3D//XDpUpklpGuybToeXR4AK4S4c4pSwMmTz5CZeYLF\nl9uy48IvDG7+tNZpCSEcgNls/TNtGkRFwfTp1ttNJuufG3nxRUhJsZ7dZutWqFSpLDMVwv7JmI0Q\n5VjMglh2pPjy2CTPYvcdOzacnNxLfHjKh4sZSSx/bDkV3SpqkKUQQtwaRYFJk6zjNhs3gre31hmJ\n8koPfacux2xCQkIKv0Yym81FvlKSWGKJrfG5T2J4ZryBWV/nkpNT/H6/aiN4blsi0ZfjWDtsLRXd\nKtpV/hJLLLHE14sNBujXz0zVqmYGDIDsbPvKT+LyF9sz2TPv4MxmOYWVmuylnmdnRzP6LQ/yWvmx\n+g8nKlQovmb2ztn8lfAXn/f/HCejk+2TvAl7qWV5IfVUl9RTPXdSy4ICGDYMMjOtZ7txcVE3Nz2S\nbVNdeug7dblnXghxfZEzz/H0WxUw3u3H2o0lN/IAEztPZMGDC+yykRdCiNJwcoLFi8FigZEjrc29\nEI5G9swLUY5kHMsg5L50cjpU4ecVTri5WW9XlAIMBmnahRDlU1YW9O0LDRvC55//c8pLIe6UHvpO\naeaFKGcunLdQzd9Y+HVzVtZZjhx5iBYt1spFoYQQ5VZamvWUld26wfvvS0Mv1KGHvlPGbBycXg7u\n0At7qGetOv808hkZxzl4sCvpbt1JyXfVNrFbZA+1LE+knuqSeqpHrVp6e8Pvv1v/vPuuKk+pS7Jt\nOh5p5oUop9LS9hMR0Z0414d4dP1SzqWc0zolIYQoU5UqwR9/WK8UO3eu1tkIYRsyZiOETikWhfiI\nLKq09iz2dXJKyk6OHBnIGePDTN69ht+H/U7L6i21SVQIIWwsKgq6doWZM60Hxgpxu/TQd+pyz7yc\nZ15iR48Vi8LuJ0/RqYuF9987VOz+vLwU9uU9wORta5jdZHZhI28v+UssscQSl2UcFARvv23mpZfM\nhIZqn4/E+o/tmeyZd3Bms5yPVk22qKdSoLD98UhGranNY+PcePs9Y7E98ytPrmSaeRq/D/udGl41\nyjSfsiLbprqknuqSeqqnLGt54AD07m09fWWvXmXyEnZHtk116aHv1OWeeSEclSXfwpZBkYxYU4eR\nr7jxzvvFG3mABxs+yI5RO3TbyAshhBratIHQUBg+HHbu1DobIcqG7JkXQkc2PBTJUxvr8MJ/XHh1\ninwWF0KI0vjjD3jySVi3ztrgC1Faeug7pZkXQkeO/5bC7nhvnnrmn0b+/Pn38fJqS6VKPTXMTAgh\n7FtoKDz/PGzZAo0ba52N0As99J2ya8/B6eXgDr0o63o2GehT2MgrisLp05OJif2GFItvmb6uFmTb\nVJfUU11ST/XYqpaDBsF771ln56OibPKSmpBt0/FIMy+EDimKhcjI57kYt5pnwjPYEn1Y65SEEMLu\njRwJr75qvVLsxYtaZyOEOmTMRgg7pSgKhhKObrVY8jl5chRxKUcI2R3Lf0zvMarNKA0yFEIIfXrn\nHfjxR9i61XqhKSGuRw99p+yZF8IO5afm8/09p5gyJrfYfRkZR7iYepbHd0Yzp++X0sgLIcQtev11\n6NPH+ictTetshLgz0sw7OJmtU5ca9cxLzmNhx9OMP1yXLn1dit0fk+3GY9v+YukjoTzU6KE7fj17\nJdumuqSe6pJ6qkeLWhoM8P771jPbPPQQZGXZPIUyI9um43HWOoHbERISQkhICCaTqXCjvXqBBIlv\nLT548KBd5aP3+I7rudLM/ud9eTuhOT+ucMLVdStmc9H1iqJwYPQBalWspfn7lVhiiSW+0/gqW7/+\n1q1mhgyBb74xMXQojB9vxtlZ+3rotZ7lNdYDmZkXwk7kxufy+d1neevSXfy61ojJVMLVoIQQQqgq\nLw8GD4YKFWDJEnBy0jojYU/00HcatU5ACGGVeiCDH5U6rNr4TyOfnR1DQsJajTMTQojyy8UFli2D\ny5dhzBiw875NiGKkmXdwevoaSQ/upJ5Vevqx84wHnTtbG/nMzFMcOHAvsYlhKmWnL7JtqkvqqS6p\np3rsoZbu7rBiBRw6ZD11pZ4benuop7AtaeaFsCNXz0SZnn6EAwe7seKSF5//laBtUkII4QC8vWHt\nWli/3nrqSiH0QmbmhbAzqanhHDrcnyUxPmS7dearh77C2ajLY9WFEEJ3Ll2C++6DF16AF1/UOhuh\ntZL6zrZt2+Lj4wNAvXr1mDJlCiEhIRiNRpo3b868efNKvE5MWZE980JoIOtMFitmp2KxFL29oCCb\nw0eG8L/T7nj5PcQ3A76RRl4IIWyoRg3YuBE+/BAWLtQ6G2FvsrOzAdiyZQtbtmzh66+/5uWXX+bd\nd99l27ZtKIrCihUrbJqTNPMOTmbr1FWaemb+lclr7S7z4geexMcXvS89L4en9ym0rf88H/b6EKPB\ncf+LyrapLqmnuqSe6rHHWgYGwoYNMGUK/PKL1tncGnusZ3kSERFBZmYmDzzwAD169CAsLIz9+/fT\ntWtXAPr06cPGjRttmpPs8hPChjKOZ/Byx3g2uAewY68z1aoVvd/H3YeljyynjX8bbRIUQggBQKNG\n8Pvv0KsXeHlB795aZyTsQYUKFZg0aRJPP/00kZGR9P7XhuHl5UVKSopNc5KZeSFsJO1QOi92SWJn\nRX+27nXG31/rjIQQQtzMrl0wYACEhlpn6YVj+XffmZubi8Viwd3dHYC7776bAwcOkJeXB8CKFSvY\nuHEjc+fOtVmOsmdeCBuw5FiYHhzHnkq12RH+zx75jIzjVKjQRNvkhBBCXFfnzvDDD9YLS61bB23b\nap2RKEtms/mGo0rffvsthw4dYt68ecTGxpKWlkavXr3YunUr3bp14/fff6dHjx62SxjZM+/wzGZz\n4aWLxZ27UT1jT+biVsWVypVBURTOnZvJxcs/cE+HQxiNrrZNVAdk21SX1FNdUk/16KWWv/0GY8fC\n5s3QxI73weilnnrx774zPz+fUaNGce7cOQA++OADKleuzDPPPENubi5Nmzblyy+/tOnZbGTPvBA2\nUrORtWFXFIXTp1/hZMwPfHquNuvudtE4MyGEEDczcCCkpVln6Ldtg7p1tc5IaMHZ2ZnFixcXu13L\nA49lz7wQNqQoBZz86zmOXVjN+5G+/PbEJmp619Q6LSGEEKU0bx589BFs3w415cd3uaeHvlP2zAtR\nBrLi8nCt5IKTU9HbT54cy/7oVXwVE8S6Eb/j5+GnTYJCCCFuy/PPQ0qKdQ/91q1QubLWGQlHp8tm\nPiQkhJCQEEwmU+HXGlfnwyS+tXjOnDm0bt3abvLRc6woCoN6DmJM9/G8O7MFA6b70uae7YX3WxQL\nU7btIT43kPWjN+Pp4mlX+dtbfO1XlvaQj95jqafU017jq7fZSz6liadMgcOHzXTuDOHhJipWtJ/8\nrt5mL/noPdYDGbNxcGazuXDDFXdm1c+rmPjYezixiHpdAgnd4IKbW9E1ayPX0rNeT1ycZE7+ZmTb\nVJfUU11ST/XotZaKYj0g9tgx61luPDy0zshKr/W0V3roO6WZF+IOLfxiIYs/XUz+hYrsSKlDZZcG\nNLtrJSPHP0HI6BCt0xNCCFFGLBZ48klITrae7cbVVeuMhNr00HcatU5ACL3LwYkjUbnsSLkLC3PI\nthzgdFoOWRb7/s8vhBDizhiNsHAhODtbm/qCAq0zEo5ImnkHp6eZMHv17LPDGTrgfpzJBgzkWww8\nNrgjbdrMIj39sNbp6ZZsm+qSeqpL6qkevdfSxQV++gni4+G556zjN1rSez3FrZNmXog7ZDAYyCIR\nJw8FX9+h4ObKiaM/sPhsKs7uDbROTwghRBlzd4cVK+DwYXjlFe0beuFYZGZeiFJSChT++vIKOXdX\npWXbop+DZ836ktTUGI6feBcv74ocynTjk9nfE1w3WKNshRBC2FpiIgQHw+DB8OabWmcj1KCHvlP2\nzAtxE4qicGF5AuNrRdPpxUr8+kPRociFC79g48Y5nDs3j/Ev5pOYnoR3nAfntv6lUcZCCCG0UKkS\n/PEHLFkCn3yidTbCUUgz7+Bktu7GkvemMaNZNO2GeHE6qDo7Djrz1odFTyuZbVE4n5BCckYiBgNY\n8oxcScklWw6AvSOybapL6qkuqad6ylstq1eHjRutV4n95hvbv355q6e4OV1eNEoIW0jbn0b/e/PI\nr12dXze70OW+kj/7jh41muhzkRw88BGTZkM1o4XHHn6U0aNG2zhjIYQQ9qBOHdiwAUwm8PaGIUO0\nzkiUZzIzL8R1KIpC7FkLNes6YTD8c3teXiKKUoCra9XC28aMG8ZPu0OpUbMul2LP8ljnwXw2d4kG\nWQshhLAXERHQqxcsWgS9e2udjbgdeug7ZcxGiOswGAzUqvdPI5+fn0ZU1Ex27q5HYuK6ImvrNGjO\nl9OXcHTlUb6cvoQ6DZprkLEQQgh70qoVLF8OI0bA9u1aZyPKK2nmHZyjz9YpisK+z+J5pkc62dkl\nrykoyCI6+iN27g5i9dH5TD7qTYFn9yJrpoyfwuAHB7N161YGPziYyS9OtkH25Zujb5tqk3qqS+qp\nnvJey06d4IcfrGe42bev7F+vvNdTFCfNvHBYZ9ak8mTNK/R40Refmk4lXrmvoCCLXWEN2XjsI145\nZMBQdSrbnj1NrYq1bJ+wEEIIXbr/fvjyS+jfH44d0zobUd7IzLxwOHEHM5nxRBqLT1ZiYHAu737n\niX9NQ4lrt5zdwpgVgxjRbhLj7xlPBdcKNs5WCCFEebFkCUyZAtu2Qd26WmcjSkMPfac088LhLOh5\nntVXKjP7e3caNXe64drcglzSc9Op5FHJRtkJIYQozz77DP77X+sMfc2aWmcjbkYPfaeM2Tg4R5yt\ne3ZDHVZGVChs5BVFISFhLWfOvF5srauT6y018o5Yz7IitVSX1FNdUk/1OFotx46FZ56Bnj0hPl79\n53e0ego5z7wo5woKwOkGO9+TksycOTuVuLQz5Hg/QT3bpSaEEMJBTZ4MKSnW01Vu3gwVK2qdkdAz\nuxqziYiI4IUXXqB+/fqMHDkSk8lUbI0evu4Q2ts8L4Uprxt46HkPpr7rUuz+1NRwzpx5nfjUQ3wb\nZSHJqR2zerxPqxqtNMhWCCGEo1EUGDcOjhyB338HT0+tMxIl0UPfaVdjNn/++Sf+/v44OzvTrFkz\nrdMROhSxOoPe/kk8Ot6DwY8amDS9+JdPiqKw+9TnfH78GNMig3im68+sHbZOGnkhhBA2YzDA3LnW\nq8U+8gjk5mqdkdAru2rm7733Xr766iteffVVPvzwQ63TcQjlZbYu80IOTzRJottDrrRsb+RMvDOv\nLvDG1bX4WWoUFL49k0GfNp+x46kwTEEm1fIoL/W0B1JLdUk91SX1VI8j19JohG+/BVdXGD6cEk+R\nfKscuZ6Oqsyb+T179hAcHAyAxWLhueeeo3PnzgQHB3P69GkA3nzzTR5//HEOHjxIQUEBvr6+5Ofn\nl3Vqohwx5FloWS+fE6cMfLDKB29f66adk3Op2NdjRoORHx/5kQGNB2AwlHxKSiGEEMIWnJ3hxx8h\nMRFGj7aO39wqsxmmT7fu7Q8Otv57+nTr7aL8K9OZ+Q8++IAlS5bg5eXFrl27CA0NZfXq1XzzzTfs\n2bOHWbNmsXz58sL1u3fv5rPPPsPFxYVp06YRGBhYPGEdzC4J7eXmXubcuXe5dHkJ7duF4+Ehh7YK\nIYSwX+np0KsXdOxoPXXl7exruvoYaZPUo4e+s0z3zN91112EhoYWFmHHjh307t0bgHvuuYe9e/cW\nWd+pUycWL17MN998U2IjL0R+vsKuLdf/1iYvL5EzZ6YQtqcJW6K28PJhP1zdZFsSQghh37y8YM0a\n69ltZszQOhuhJ2XazA8aNAhn538OQExLS6PiNedfcnJywmKxlGUK4ib0MlunKLDsvxk08cnmpeG5\nJc4VpqSEEbanAbuifueZfRb+Unrx+8gwnIw3vjCUmvRSTz2QWqpL6qkuqad6pJb/8POD9evhhx/g\n449v91nMKmYk9MCm55mvWLEiaWlphbHFYsFovPXPEyEhIQQFBQHg6+tL69atC09jefWHgsSliw8e\nPGhX+ZQUHzVX5Nt5jUhINvD0g6foMjYBJ6fi69edj2T6n7nUrxDAxlGrqO1TW+opscQSS2zn8VX2\nko89xBs2wN13m4mNhdmzb+3xV9nT+9FzrAdlfp75qKgoHn/8cXbv3k1oaCirVq3i22+/JSwsjJkz\nZ7JmzZpbej49zC4J9Ux7IJ7PN3rxyoB0xi/yw9X7+nvZj1w5grPRmcZVGtswQyGEEEJ9f/0FJhPM\nmQNDh5buMTIzrz499J022TN/9YwhAwcOZMOGDXTp0gWAb7/91hYvL3TsyaeNvLzAiE9gFQAUpYDL\nl7/H2bkSVar0L7K2ebXmWqQohBBCqK5hQ1i3Dnr2tM7T9+2rdUbCXhnL+gWCgoLYtWsXYG3q58+f\nz86dO9m5cycNGzYs65cXN2HvXyPdNbQSPoGuKIqFK1d+ITy8BUfPfEBavn2eUtLe66knUkt1ST3V\nJfVUj9Ty+lq2hBUrYORI2Lq1tI8yl2FGwh7ZdGZeLSEhIYSEhGAymexmpkqvsT3MeGdnGwlbdTcD\n/s+NlJSt/7p/C/AnXl7LSM9NZ/7xPPYkFhBa15+6GuV7o9ge6imxxBJLrKf4KnvJxx7jH3+EAQPM\nvP8+jB594/VX2VP+eo71oMxn5tWmh9klUTr5+TD/7Rzeft9AM6c05m30oklHtyJrLJZ8du/vRWh0\nKr+cu8Jbphk82fJJm56hRgghhNDaihXWi0pt2gTNmpW8Rmbm1aeHvlOXe+aFvikK/PR1HlNeUaiU\nmcVXz+fQ772qGN2MxdZeyrjCkK0neLXLq5x88Dncnd01yFgIIYTQ1oAB1gtLPfAAbNsG9eppnZGw\nF8W7J+FQtPga6dwfqbw3JpM3e8az+1IFHvy4OkY3I/n5acXW1vSuSdSEKCZ0nKCLRl5PX8vZO6ml\nuqSe6pJ6qkdqWXrDhsEbb8D998OFC9dbZbZhRsIe6HLPvAPT65kAACAASURBVMzMqxdrMuNthLBT\nHXEP9Pn7/gtUq7aWjIyjZGR8DBjspj66qKfEEksssY7jq+wlH3uPn3vOREoKdO5s5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4qt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\nP06fPg17e/s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       "prompt_number": 28,
       "text": [
        "<matplotlib.figure.Figure at 0x7fe3d6b9f7d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OLEfmva2scLC3Z3RICNXq1oVy5Qjq1AkqV0Z79ChoNEZfl9RcYlkX3bCx1NNw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4qt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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe3d6b9f7d0>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html import widgets\n",
      "from IPython.html.widgets import interact\n",
      "interact(read_results_and_plot_ber, \n",
      "         scenario_string=['2x2_(1)', '4x4_(2)'],\n",
      "         max_iterations=[5, 120, 5])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<function __main__.read_results_and_plot_ber>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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oW9Z0q1fv7k+SlQUXL97exEdHw+7duZv6a9duNuv3Wp13dZVmXgghhBBCPDCb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\ng8jISLRq1QqnT5823y8mJgZdu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       "text": [
        "<matplotlib.figure.Figure at 0x7fe3d67496d0>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plot the Sum Capacity"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# StaticInteract(read_results_and_plot_capacity,\n",
      "#                scenario_string=RadioWidget(scenarios),\n",
      "#                max_iterations=RangeWidget(5, 120, 10),)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html import widgets\n",
      "from IPython.html.widgets import interact\n",
      "interact(read_results_and_plot_capacity, \n",
      "         scenario_string=['2x2_(1)', '4x4_(2)'],\n",
      "         max_iterations=[5, 120, 5])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PADx8+BA///wzfv/9d4SHhyM8PBxRUVH48MMP8e2332q3U6lUaN++PTZv3qxdjV65cqW2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QMYOxbc3bU2kxMnoGVLyyq8Adr36EHvrl0xAgbAWKAAX3z3He179NA7NCGEEC8hxXc6Jn1hpiX5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\niIiISCUsvomIiIiIVPL/hDexv3Qvj/IAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe3d468dad0>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Apagar depois"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pyphysim.plot.pgfplotshelper import generate_pgfplots_plotline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#def plot_ber(alt_min_results, max_sinrn_results, mmse_results, max_iterations, ax=None):\n",
      "def plot_ber_tikz(max_iterations=5, ax=None, alt_min_results=None, max_sinrn_results=None, mmse_results=None):\n",
      "    SNR_alt_min = np.array(alt_min_results.params['SNR'])\n",
      "    SNR_max_SINR = np.array(max_sinrn_results.params['SNR'])\n",
      "    # SNR_min_leakage = np.array(min_leakage_results.params['SNR'])\n",
      "    SNR_mmse = np.array(mmse_results.params['SNR'])\n",
      "    \n",
      "    # Alt. Min. Algorithm\n",
      "    ber_alt_min = alt_min_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_CF_alt_min = alt_min_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_errors_alt_min = np.abs([i[1] - i[0] for i in ber_CF_alt_min])\n",
      "\n",
      "    # Max SINR Algorithm (random init)\n",
      "    ber_max_sinr = max_sinrn_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    ber_CF_max_sinr = max_sinrn_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    ber_errors_max_sinr = np.abs([i[1] - i[0] for i in ber_CF_max_sinr])\n",
      "\n",
      "    # Max SINR Algorithm (alt_min init)\n",
      "    ber_max_sinr2 = max_sinrn_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    ber_CF_max_sinr2 = max_sinrn_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    ber_errors_max_sinr2 = np.abs([i[1] - i[0] for i in ber_CF_max_sinr2])\n",
      "    \n",
      "    # MMSE Algorithm (random init)\n",
      "    ber_mmse = mmse_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    ber_CF_mmse = mmse_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    ber_errors_mmse = np.abs([i[1] - i[0] for i in ber_CF_mmse])\n",
      "    \n",
      "    # MMSE Algorithm (alt_min init)\n",
      "    ber_mmse2 = mmse_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    ber_CF_mmse2 = mmse_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    ber_errors_mmse2 = np.abs([i[1] - i[0] for i in ber_CF_mmse2])\n",
      "\n",
      "#     if ax is None:\n",
      "#         fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12,9))\n",
      "    \n",
      "#     ax.errorbar(SNR_alt_min, ber_alt_min, ber_errors_alt_min, fmt='-r*', elinewidth=2.0, label='Alt. Min.')\n",
      "#     ax.errorbar(SNR_max_SINR, ber_max_sinr, ber_errors_max_sinr, fmt='-g*', elinewidth=2.0, label='Max SINR (random)')\n",
      "#     ax.errorbar(SNR_max_SINR, ber_max_sinr2, ber_errors_max_sinr2, fmt='-y*', elinewidth=2.0, label='Max SINR (alt_min)')\n",
      "#     ax.errorbar(SNR_mmse, ber_mmse, ber_errors_mmse, fmt='-m*', elinewidth=2.0, label='MMSE (random)')\n",
      "#     ax.errorbar(SNR_mmse, ber_mmse2, ber_errors_mmse2, fmt='-b*', elinewidth=2.0, label='MMSE (alt_min)')\n",
      "\n",
      "#     ax.set_xlabel('SNR')\n",
      "#     ax.set_ylabel('BER')\n",
      "#     title = 'BER for Different Algorithms ({max_iterations} Max Iterations)\\nK={K}, Nr={Nr}, Nt={Nt}, Ns={Ns}, {M}-{modulator}'.replace(\"{max_iterations}\", str(max_iterations))\n",
      "#     ax.set_title(title.format(**alt_min_results.params.parameters))\n",
      "\n",
      "#     ax.set_yscale('log')\n",
      "#     leg = ax.legend(fancybox=True, shadow=True, loc='lower left', bbox_to_anchor=(0.01, 0.01), ncol=4)\n",
      "#     ax.grid(True, which='both', axis='both')\n",
      "    \n",
      "#     Lets plot the mean number of ia iterations\n",
      "#     ax2 = ax.twinx()\n",
      "    mean_alt_min_ia_terations = get_num_mean_ia_iterations(alt_min_results, {'max_iterations': max_iterations})\n",
      "    mean_max_sinrn_ia_terations = get_num_mean_ia_iterations(max_sinrn_results, {'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    mean_max_sinrn_ia_terations2 = get_num_mean_ia_iterations(max_sinrn_results, {'max_iterations': max_iterations, 'initialize_with': 'alt_min'})    \n",
      "    mean_mmse_ia_terations = get_num_mean_ia_iterations(mmse_results, {'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    mean_mmse_ia_terations2 = get_num_mean_ia_iterations(mmse_results, {'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "#     ax2.plot(SNR_alt_min, mean_alt_min_ia_terations, '--r*')\n",
      "#     ax2.plot(SNR_max_SINR, mean_max_sinrn_ia_terations, '--g*')\n",
      "#     ax2.plot(SNR_max_SINR, mean_max_sinrn_ia_terations2, '--y*')\n",
      "#     ax2.plot(SNR_mmse, mean_mmse_ia_terations, '--m*')\n",
      "#     ax2.plot(SNR_mmse, mean_mmse_ia_terations2, '--b*')\n",
      "    \n",
      "#     # Horizontal line with the max alowed ia iterations\n",
      "#     ax2.hlines(max_iterations, SNR_alt_min[0], SNR_alt_min[-1], linestyles='dashed')\n",
      "#     ax2.set_ylim(0, max_iterations*1.1)\n",
      "#     ax2.set_ylabel('IA Mean Iterations')\n",
      "\n",
      "#     # Set the X axis limits\n",
      "#     ax.set_xlim(SNR_alt_min[0], SNR_alt_min[-1])\n",
      "#     # Set the Y axis limits\n",
      "#     ax.set_ylim(1e-6, 1)\n",
      "    \n",
      "#     fig = ax.figure\n",
      "    \n",
      "    # Plot the BER\n",
      "    alt_min_line = generate_pgfplots_plotline(\n",
      "        SNR_alt_min, ber_alt_min, #ber_errors_alt_min,\n",
      "        options='alt min style', \n",
      "        legend=\"IA-Alternating\")\n",
      "    max_sinr_random_init_line = generate_pgfplots_plotline(\n",
      "        SNR_max_SINR, ber_max_sinr, #ber_errors_alt_min,\n",
      "        options='max sinr random init style',\n",
      "        legend=\"IA-Max SINR (random)\")\n",
      "    max_sinr_alt_init_line = generate_pgfplots_plotline(\n",
      "        SNR_max_SINR, ber_max_sinr2, #ber_errors_alt_min,\n",
      "        options='max sinr alt init style',\n",
      "        legend=\"IA-Max SINR (alt init)\")\n",
      "    mmse_random_init_line = generate_pgfplots_plotline(\n",
      "        SNR_mmse, ber_mmse, #ber_errors_alt_min,\n",
      "        options='mmse random init style',\n",
      "        legend=\"IA-MMSE (random)\")\n",
      "    mmse_alt_init_line = generate_pgfplots_plotline(\n",
      "        SNR_mmse, ber_mmse2, #ber_errors_alt_min,\n",
      "        options='mmse alt init style',\n",
      "        legend=\"IA-MMSE (alt init)\")\n",
      "    \n",
      "    # Plot mean IA iterations\n",
      "    alt_min_iter_line = generate_pgfplots_plotline(\n",
      "        SNR_alt_min, mean_alt_min_ia_terations, #ber_errors_alt_min,\n",
      "        options='alt min iter style')\n",
      "    max_sinr_random_init_iter_line = generate_pgfplots_plotline(\n",
      "        SNR_max_SINR, mean_max_sinrn_ia_terations, #ber_errors_alt_min,\n",
      "        options='max sinr random init iter style')\n",
      "    max_sinr_alt_init_iter_line = generate_pgfplots_plotline(\n",
      "        SNR_max_SINR, mean_max_sinrn_ia_terations2, #ber_errors_alt_min,\n",
      "        options='max sinr alt init iter style')\n",
      "    mmse_random_init_iter_line = generate_pgfplots_plotline(\n",
      "        SNR_mmse, mean_mmse_ia_terations, #ber_errors_alt_min,\n",
      "        options='mmse random init iter style')\n",
      "    mmse_alt_init_iter_line = generate_pgfplots_plotline(\n",
      "        SNR_mmse, mean_mmse_ia_terations2, #ber_errors_alt_min,\n",
      "        options='mmse alt init iter style')\n",
      "    \n",
      "    ber_plots = (alt_min_line, max_sinr_random_init_line, max_sinr_alt_init_line, mmse_random_init_line, mmse_alt_init_line)\n",
      "    mean_iterations_plots = (alt_min_iter_line, max_sinr_random_init_iter_line, max_sinr_alt_init_iter_line, mmse_random_init_iter_line, mmse_alt_init_iter_line)\n",
      "    return ber_plots, mean_iterations_plots"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scenario = \"4x4_(2)\"\n",
      "max_iterations = 80\n",
      "\n",
      "alt_min_results, max_sinr_results, mmse_results = read_results(scenario)\n",
      "ber_plots, mean_iterations_plots = plot_ber_tikz(max_iterations=max_iterations, \n",
      "                                                 ax=None, \n",
      "                                                 alt_min_results=alt_min_results,\n",
      "                                                 max_sinrn_results=max_sinr_results, \n",
      "                                                 mmse_results=mmse_results)\n",
      "\n",
      "print \"% Max Iterations: {0}\\n\".format(max_iterations)\n",
      "\n",
      "print \"% xxxxxxxxxxxxxxxxxxxx BER PLOTS xxxxxxxxxxxxxxxxxxxx\"\n",
      "for p in ber_plots:\n",
      "    print p\n",
      "    print\n",
      "    \n",
      "print\n",
      "print \"% xxxxxxxxxxxxxxxxxxxx Mean Iterations PLOTS xxxxxxxxxxxxxxxxxxxx\"\n",
      "for p in mean_iterations_plots:\n",
      "    print p\n",
      "    print\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "% Max Iterations: 80\n",
        "\n",
        "% xxxxxxxxxxxxxxxxxxxx BER PLOTS xxxxxxxxxxxxxxxxxxxx\n",
        "\\addplot[alt min style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 0.4219392156862745)\n",
        "(-5.0, 0.36411454248366015)\n",
        "(0.0, 0.276643954248366)\n",
        "(5.0, 0.16808039215686274)\n",
        "(10.0, 0.0782640522875817)\n",
        "(15.0, 0.030913235294117648)\n",
        "(20.0, 0.011761352657004832)\n",
        "(25.0, 0.005544444444444445)\n",
        "(30.0, 0.0034017622461170846)};\n",
        "\\addlegendentry{IA-Alternating};\n",
        "\n",
        "\\addplot[max sinr random init style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 0.3452484413017032)\n",
        "(-5.0, 0.23515811446040563)\n",
        "(0.0, 0.10208593469094238)\n",
        "(5.0, 0.03058267973856209)\n",
        "(10.0, 0.005652450980392157)\n",
        "(15.0, 0.0008084876543209877)\n",
        "(20.0, 0.00028725957531917733)\n",
        "(25.0, 0.0006504500375031253)\n",
        "(30.0, 0.001383647568847917)};\n",
        "\\addlegendentry{IA-Max SINR (random)};\n",
        "\n",
        "\\addplot[max sinr alt init style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 0.34634222711401247)\n",
        "(-5.0, 0.23507477649224295)\n",
        "(0.0, 0.10235340262796627)\n",
        "(5.0, 0.030620098039215685)\n",
        "(10.0, 0.005963235294117647)\n",
        "(15.0, 0.000978292181069959)\n",
        "(20.0, 0.00021746632996632996)\n",
        "(25.0, 5.797916666666667e-05)\n",
        "(30.0, 2.1145833333333333e-05)};\n",
        "\\addlegendentry{IA-Max SINR (alt init)};\n",
        "\n",
        "\\addplot[mmse random init style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 0.33190942862197065)\n",
        "(-5.0, 0.21249233861666722)\n",
        "(0.0, 0.09596556126624546)\n",
        "(5.0, 0.021915658711997386)\n",
        "(10.0, 0.0015278140933455355)\n",
        "(15.0, 5.2241138594261186e-05)\n",
        "(20.0, 1.3253589513826662e-05)\n",
        "(25.0, 2.742615154407566e-05)\n",
        "(30.0, 1.264999729077488e-05)};\n",
        "\\addlegendentry{IA-MMSE (random)};\n",
        "\n",
        "\\addplot[mmse alt init style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 0.33248203349028216)\n",
        "(-5.0, 0.21242481203007518)\n",
        "(0.0, 0.09498739894609368)\n",
        "(5.0, 0.02195731747173018)\n",
        "(10.0, 0.001605718954248366)\n",
        "(15.0, 3.5125e-05)\n",
        "(20.0, 3.541666666666667e-07)\n",
        "(25.0, 0.0)\n",
        "(30.0, 0.0)};\n",
        "\\addlegendentry{IA-MMSE (alt init)};\n",
        "\n",
        "\n",
        "% xxxxxxxxxxxxxxxxxxxx Mean Iterations PLOTS xxxxxxxxxxxxxxxxxxxx\n",
        "\\addplot[alt min iter style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 80.0)\n",
        "(-5.0, 80.0)\n",
        "(0.0, 80.0)\n",
        "(5.0, 80.0)\n",
        "(10.0, 80.0)\n",
        "(15.0, 80.0)\n",
        "(20.0, 80.0)\n",
        "(25.0, 80.0)\n",
        "(30.0, 80.0)};\n",
        "\n",
        "\\addplot[max sinr random init iter style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 75.56411764705882)\n",
        "(-5.0, 79.6721568627451)\n",
        "(0.0, 80.0)\n",
        "(5.0, 80.0)\n",
        "(10.0, 80.0)\n",
        "(15.0, 80.0)\n",
        "(20.0, 80.0)\n",
        "(25.0, 80.0)\n",
        "(30.0, 80.0)};\n",
        "\n",
        "\\addplot[max sinr alt init iter style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 76.14372549019608)\n",
        "(-5.0, 79.6821568627451)\n",
        "(0.0, 80.0)\n",
        "(5.0, 80.0)\n",
        "(10.0, 80.0)\n",
        "(15.0, 80.0)\n",
        "(20.0, 80.0)\n",
        "(25.0, 80.0)\n",
        "(30.0, 80.0)};\n",
        "\n",
        "\\addplot[mmse random init iter style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 61.22372549019608)\n",
        "(-5.0, 49.56058823529412)\n",
        "(0.0, 72.21039215686274)\n",
        "(5.0, 79.95117647058824)\n",
        "(10.0, 80.0)\n",
        "(15.0, 80.0)\n",
        "(20.0, 80.0)\n",
        "(25.0, 80.0)\n",
        "(30.0, 80.0)};\n",
        "\n",
        "\\addplot[mmse alt init iter style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 61.91137254901961)\n",
        "(-5.0, 50.86156862745098)\n",
        "(0.0, 72.59725490196078)\n",
        "(5.0, 79.95823529411764)\n",
        "(10.0, 80.0)\n",
        "(15.0, 80.0)\n",
        "(20.0, 80.0)\n",
        "(25.0, 80.0)\n",
        "(30.0, 80.0)};\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_capacity_tikz(max_iterations, ax=None, alt_min_results=None, max_sinrn_results=None, mmse_results=None):\n",
      "    SNR_alt_min = np.array(alt_min_results.params['SNR'])\n",
      "    SNR_max_SINR = np.array(max_sinrn_results.params['SNR'])\n",
      "    # SNR_min_leakage = np.array(min_leakage_results.params['SNR'])\n",
      "    SNR_mmse = np.array(mmse_results.params['SNR'])\n",
      "    \n",
      "    # xxxxx Plot Sum Capacity (all) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "    sum_capacity_alt_min = alt_min_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_CF_alt_min = alt_min_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_errors_alt_min = np.abs([i[1] - i[0] for i in sum_capacity_CF_alt_min])\n",
      "    \n",
      "#     sum_capacity_closed_form = closed_form_results.get_result_values_list(\n",
      "#         'sum_capacity',\n",
      "#         fixed_params={'max_iterations': max_iterations})\n",
      "#     sum_capacity_CF_closed_form = closed_form_results.get_result_values_confidence_intervals(\n",
      "#         'sum_capacity',\n",
      "#         P=95,\n",
      "#         fixed_params={'max_iterations': max_iterations})\n",
      "#     sum_capacity_errors_closed_form = np.abs([i[1] - i[0] for i in sum_capacity_CF_closed_form])\n",
      "    \n",
      "    # Max SINR Algorithm (random)\n",
      "    sum_capacity_max_sinr = max_sinrn_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    sum_capacity_CF_max_sinr = max_sinrn_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    sum_capacity_errors_max_sinr = np.abs([i[1] - i[0] for i in sum_capacity_CF_max_sinr])\n",
      "    \n",
      "    # Max SINR Algorithm (alt_min)\n",
      "    sum_capacity_max_sinr2 = max_sinrn_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    sum_capacity_CF_max_sinr2 = max_sinrn_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    sum_capacity_errors_max_sinr2 = np.abs([i[1] - i[0] for i in sum_capacity_CF_max_sinr2])\n",
      "\n",
      "    # MMSE Algorithm (random)\n",
      "    sum_capacity_mmse = mmse_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    sum_capacity_CF_mmse = mmse_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    sum_capacity_errors_mmse = np.abs([i[1] - i[0] for i in sum_capacity_CF_mmse])\n",
      "    \n",
      "    # MMSE Algorithm (alt_min)\n",
      "    sum_capacity_mmse2 = mmse_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    sum_capacity_CF_mmse2 = mmse_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    sum_capacity_errors_mmse2 = np.abs([i[1] - i[0] for i in sum_capacity_CF_mmse2])\n",
      "    \n",
      "    # Lets plot the mean number of ia iterations\n",
      "    mean_alt_min_ia_terations = get_num_mean_ia_iterations(alt_min_results, {'max_iterations': max_iterations})\n",
      "    mean_max_sinrn_ia_terations = get_num_mean_ia_iterations(max_sinrn_results, {'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    mean_max_sinrn_ia_terations2 = get_num_mean_ia_iterations(max_sinrn_results, {'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "    mean_mmse_ia_terations = get_num_mean_ia_iterations(mmse_results, {'max_iterations': max_iterations, 'initialize_with': 'random'})\n",
      "    mean_mmse_ia_terations2 = get_num_mean_ia_iterations(mmse_results, {'max_iterations': max_iterations, 'initialize_with': 'alt_min'})\n",
      "        \n",
      "    # Plot the Sum Capacity\n",
      "    alt_min_line = generate_pgfplots_plotline(\n",
      "        SNR_alt_min, sum_capacity_alt_min, #ber_errors_alt_min,\n",
      "        options='alt min style', \n",
      "        legend=\"IA-Alternating\")\n",
      "    max_sinr_random_init_line = generate_pgfplots_plotline(\n",
      "        SNR_max_SINR, sum_capacity_max_sinr, #ber_errors_alt_min,\n",
      "        options='max sinr random init style',\n",
      "        legend=\"IA-Max SINR (random)\")\n",
      "    max_sinr_alt_init_line = generate_pgfplots_plotline(\n",
      "        SNR_max_SINR, sum_capacity_max_sinr2, #ber_errors_alt_min,\n",
      "        options='max sinr alt init style',\n",
      "        legend=\"IA-Max SINR (alt init)\")\n",
      "    mmse_random_init_line = generate_pgfplots_plotline(\n",
      "        SNR_mmse, sum_capacity_mmse, #ber_errors_alt_min,\n",
      "        options='mmse random init style',\n",
      "        legend=\"IA-MMSE (random)\")\n",
      "    mmse_alt_init_line = generate_pgfplots_plotline(\n",
      "        SNR_mmse, sum_capacity_mmse2, #ber_errors_alt_min,\n",
      "        options='mmse alt init style',\n",
      "        legend=\"IA-MMSE (alt init)\")\n",
      "    \n",
      "    # Plot mean IA iterations\n",
      "    alt_min_iter_line = generate_pgfplots_plotline(\n",
      "        SNR_alt_min, mean_alt_min_ia_terations, #ber_errors_alt_min,\n",
      "        options='alt min iter style')\n",
      "    max_sinr_random_init_iter_line = generate_pgfplots_plotline(\n",
      "        SNR_max_SINR, mean_max_sinrn_ia_terations, #ber_errors_alt_min,\n",
      "        options='max sinr random init iter style')\n",
      "    max_sinr_alt_init_iter_line = generate_pgfplots_plotline(\n",
      "        SNR_max_SINR, mean_max_sinrn_ia_terations2, #ber_errors_alt_min,\n",
      "        options='max sinr alt init iter style')\n",
      "    mmse_random_init_iter_line = generate_pgfplots_plotline(\n",
      "        SNR_mmse, mean_mmse_ia_terations, #ber_errors_alt_min,\n",
      "        options='mmse random init iter style')\n",
      "    mmse_alt_init_iter_line = generate_pgfplots_plotline(\n",
      "        SNR_mmse, mean_mmse_ia_terations2, #ber_errors_alt_min,\n",
      "        options='mmse alt init iter style')\n",
      "    \n",
      "    sum_capacity_plots = (alt_min_line, max_sinr_random_init_line, max_sinr_alt_init_line, mmse_random_init_line, mmse_alt_init_line)\n",
      "    mean_iterations_plots = (alt_min_iter_line, max_sinr_random_init_iter_line, max_sinr_alt_init_iter_line, mmse_random_init_iter_line, mmse_alt_init_iter_line)\n",
      "    \n",
      "    return sum_capacity_plots, mean_iterations_plots"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scenario = \"4x4_(2)\"\n",
      "max_iterations = 80\n",
      "\n",
      "alt_min_results, max_sinr_results, mmse_results = read_results(scenario)\n",
      "sum_capacity_plots, mean_iterations_plots = plot_capacity_tikz(max_iterations=max_iterations, ax=None, alt_min_results=alt_min_results,\n",
      "                 max_sinrn_results=max_sinr_results, mmse_results=mmse_results)\n",
      "\n",
      "print \"% Max Iterations: {0}\\n\".format(max_iterations)\n",
      "\n",
      "print \"% xxxxxxxxxxxxxxxxxxxx Capacity PLOTS xxxxxxxxxxxxxxxxxxxx\"\n",
      "for p in sum_capacity_plots:\n",
      "    print p\n",
      "    print\n",
      "    \n",
      "print\n",
      "print \"% xxxxxxxxxxxxxxxxxxxx Mean Iterations PLOTS xxxxxxxxxxxxxxxxxxxx\"\n",
      "for p in mean_iterations_plots:\n",
      "    print p\n",
      "    print"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "% Max Iterations: 80\n",
        "\n",
        "% xxxxxxxxxxxxxxxxxxxx Capacity PLOTS xxxxxxxxxxxxxxxxxxxx\n",
        "\\addplot[alt min style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 0.4132172613411946)\n",
        "(-5.0, 1.2086624567718252)\n",
        "(0.0, 3.124529555022003)\n",
        "(5.0, 6.908673651776635)\n",
        "(10.0, 12.861884450886947)\n",
        "(15.0, 20.1946884521351)\n",
        "(20.0, 28.385001701823107)\n",
        "(25.0, 35.921752832493304)\n",
        "(30.0, 42.34878291307027)};\n",
        "\\addlegendentry{IA-Alternating};\n",
        "\n",
        "\\addplot[max sinr random init style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 1.4865399463537972)\n",
        "(-5.0, 3.849436930302807)\n",
        "(0.0, 8.902800164150806)\n",
        "(5.0, 14.874266605477231)\n",
        "(10.0, 21.49470408414092)\n",
        "(15.0, 28.180907077431606)\n",
        "(20.0, 33.81057772196725)\n",
        "(25.0, 37.74840367565832)\n",
        "(30.0, 41.02819549963666)};\n",
        "\\addlegendentry{IA-Max SINR (random)};\n",
        "\n",
        "\\addplot[max sinr alt init style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 1.4764054165819025)\n",
        "(-5.0, 3.8471058734211905)\n",
        "(0.0, 8.895210593757897)\n",
        "(5.0, 14.866431258599452)\n",
        "(10.0, 21.50962584011127)\n",
        "(15.0, 28.387859299642397)\n",
        "(20.0, 35.42923279472478)\n",
        "(25.0, 42.57162717692349)\n",
        "(30.0, 49.51573539785626)};\n",
        "\\addlegendentry{IA-Max SINR (alt init)};\n",
        "\n",
        "\\addplot[mmse random init style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 1.6925922681169296)\n",
        "(-5.0, 4.488406450248987)\n",
        "(0.0, 8.8897994896094)\n",
        "(5.0, 14.620318072551976)\n",
        "(10.0, 21.151040177307774)\n",
        "(15.0, 27.69271218065332)\n",
        "(20.0, 33.14957059908434)\n",
        "(25.0, 37.39411937722495)\n",
        "(30.0, 41.48298172382367)};\n",
        "\\addlegendentry{IA-MMSE (random)};\n",
        "\n",
        "\\addplot[mmse alt init style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 1.682390604373995)\n",
        "(-5.0, 4.484388188202664)\n",
        "(0.0, 8.932646105685576)\n",
        "(5.0, 14.611922365740941)\n",
        "(10.0, 21.153217421733054)\n",
        "(15.0, 27.924163365169555)\n",
        "(20.0, 34.94280986236798)\n",
        "(25.0, 42.13415159661966)\n",
        "(30.0, 49.13073225127605)};\n",
        "\\addlegendentry{IA-MMSE (alt init)};\n",
        "\n",
        "\n",
        "% xxxxxxxxxxxxxxxxxxxx Mean Iterations PLOTS xxxxxxxxxxxxxxxxxxxx\n",
        "\\addplot[alt min iter style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 80.0)\n",
        "(-5.0, 80.0)\n",
        "(0.0, 80.0)\n",
        "(5.0, 80.0)\n",
        "(10.0, 80.0)\n",
        "(15.0, 80.0)\n",
        "(20.0, 80.0)\n",
        "(25.0, 80.0)\n",
        "(30.0, 80.0)};\n",
        "\n",
        "\\addplot[max sinr random init iter style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 75.56411764705882)\n",
        "(-5.0, 79.6721568627451)\n",
        "(0.0, 80.0)\n",
        "(5.0, 80.0)\n",
        "(10.0, 80.0)\n",
        "(15.0, 80.0)\n",
        "(20.0, 80.0)\n",
        "(25.0, 80.0)\n",
        "(30.0, 80.0)};\n",
        "\n",
        "\\addplot[max sinr alt init iter style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 76.14372549019608)\n",
        "(-5.0, 79.6821568627451)\n",
        "(0.0, 80.0)\n",
        "(5.0, 80.0)\n",
        "(10.0, 80.0)\n",
        "(15.0, 80.0)\n",
        "(20.0, 80.0)\n",
        "(25.0, 80.0)\n",
        "(30.0, 80.0)};\n",
        "\n",
        "\\addplot[mmse random init iter style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 61.22372549019608)\n",
        "(-5.0, 49.56058823529412)\n",
        "(0.0, 72.21039215686274)\n",
        "(5.0, 79.95117647058824)\n",
        "(10.0, 80.0)\n",
        "(15.0, 80.0)\n",
        "(20.0, 80.0)\n",
        "(25.0, 80.0)\n",
        "(30.0, 80.0)};\n",
        "\n",
        "\\addplot[mmse alt init iter style]\n",
        "plot[]\n",
        "coordinates{(-10.0, 61.91137254901961)\n",
        "(-5.0, 50.86156862745098)\n",
        "(0.0, 72.59725490196078)\n",
        "(5.0, 79.95823529411764)\n",
        "(10.0, 80.0)\n",
        "(15.0, 80.0)\n",
        "(20.0, 80.0)\n",
        "(25.0, 80.0)\n",
        "(30.0, 80.0)};\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Iterations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scenario = \"4x4_(2)\"\n",
      "max_iterations = 80\n",
      "\n",
      "alt_min_results, max_sinr_results, mmse_results = read_results(scenario)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fixed_params = {'SNR':5}\n",
      "alt_min_results.get_result_values_list('ia_runned_iterations', fixed_params=fixed_params)\n",
      "#alt_min_results.params['max_iterations']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "[1.0,\n",
        " 2.0,\n",
        " 3.0,\n",
        " 4.0,\n",
        " 5.0,\n",
        " 10.0,\n",
        " 15.0,\n",
        " 20.0,\n",
        " 25.0,\n",
        " 30.0,\n",
        " 35.0,\n",
        " 40.0,\n",
        " 45.0,\n",
        " 50.0,\n",
        " 55.0,\n",
        " 60.0,\n",
        " 65.0,\n",
        " 70.0,\n",
        " 75.0,\n",
        " 80.0,\n",
        " 85.0,\n",
        " 90.0,\n",
        " 95.0,\n",
        " 100.0,\n",
        " 105.0,\n",
        " 110.0,\n",
        " 115.0,\n",
        " 120.0,\n",
        " 200.0]"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    }
   ],
   "metadata": {}
  }
 ]
}